How to perform element-wise Boolean operations on NumPy arrays [duplicate]

Try this:

mask = (foo < 40) | (foo > 60)

Note: the __or__ method in an object overloads the bitwise or operator (|), not the Boolean or operator.

If you have comparisons within only Booleans, as in your example, you can use the bitwise OR operator | as suggested by Jcollado. But beware, this can give you strange results if you ever use non-Booleans, such as mask = (foo < 40) | override. Only as long as override guaranteed to be either False, True, 1, or 0, are you fine.

More general is the use of NumPy's comparison set operators, np.any and np.all. This snippet returns all values between 35 and 45 which are less than 40 or not a multiple of 3:

import numpy as np
foo = np.arange(35, 46)
mask = np.any([(foo < 40), (foo % 3)], axis=0)
print foo[mask]
OUTPUT: array([35, 36, 37, 38, 39, 40, 41, 43, 44])

It is not as nice as with |, but nicer than the code in your question.

You can use the NumPy logical operations. In your example:

np.logical_or(foo < 40, foo > 60)