Comparing numpy scalars directly is time consuming, use .tolist() before a comparison

This is something that I found out about recently when going through the elements of a numpy array in order to do some checks on each numbers. Turns out you shouldn't just do this

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for x in nparr:     if x == 0:         something something

as that uses a lot more time than doing this

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for x in nparr.tolist():     if x == 0:         something something

This is because a for loop iterating over a numpy array does not result in a sequence of Python constants but in a sequence of numpy scalars which would result in comparing a numpy array to a constant. Converting the array into a list first before the for loop will then result in a sequence of constants.

Here is some profiling I've done using cProfile to check different ways to do an 'if' on a numpy array element:

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import cProfile import numpy as np   runs = 1000000   print('Comparing numpy to numpy') x = np.array(1.0, np.float32) y = np.array(1.0, np.float32) cProfile.run(''' for _ in range(runs):     if x == y:         pass ''') print()   print('Comparing numpy to constant') x = np.array(1.0, np.float32) cProfile.run(''' for _ in range(runs):     if x == 1.0:         pass ''') print()   print('Comparing constant to constant') x = 1.0 cProfile.run(''' for _ in range(runs):     if x == 1.0:         pass ''') print()   print('Comparing numpy.tolist() to constant') x = np.array(1.0, np.float32) cProfile.run(''' for _ in range(runs):     if x.tolist() == 1.0:         pass ''') print()   print('Comparing numpy to numpy.array(constant)') x = np.array(1.0, np.float32) cProfile.run(''' for _ in range(runs):     if x == np.array(1.0, np.float32):         pass ''') print()   print('Comparing numpy.tolist() to numpy.tolist()') x = np.array(1.0, np.float32) y = np.array(1.0, np.float32) cProfile.run(''' for _ in range(runs):     if x.tolist() == y.tolist():         pass ''') print()

Here are the results in order of speed:

Comparing constant to constant:	0.088 seconds
Comparing numpy.tolist() to constant:	0.288 seconds
Comparing numpy.tolist() to numpy.tolist():	0.508 seconds
Comparing numpy to numpy:	0.684 seconds
Comparing numpy to constant:	1.192 seconds
Comparing numpy to numpy.array(constant):	1.203 seconds

It turns out that it is always faster to first convert your numpy scalars into constants via .tolist() than to do anything with them as numpy scalars.