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

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

as that uses a lot more time than doing this

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:

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.