I'm trying to use the logical_and of two or more numpy arrays. I know numpy has the function logical_and(), but I find the simple operator & returns the same results and are potentially easier to use.
For example, consider three numpy arrays a, b, and c. Is
np.logical_and(a, np.logical_and(b,c))
equivalent to
a & b & c?
If they are (more or less) equivalent, what's the advantage of using logical_and()?
@user1121588 answered most of this in a comment, but to answer fully...
"Bitwise and" (&) behaves much the same as logical_and on boolean arrays, but it doesn't convey the intent as well as using logical_and, and raises the possibility of getting misleading answers in non-trivial cases (packed or sparse arrays, maybe).
To use logical_and on multiple arrays, do:
np.logical_and.reduce([a, b, c])
where the argument is a list of as many arrays as you wish to logical_and together. They should all be the same shape.
I have been googling some official confirmation that I can use & instead of logical_and on NumPy bool arrays, and found one in the NumPy v1.15 Manual:
If you know you have boolean arguments, you can get away with using NumPy’s bitwise operators, but be careful with parentheses, like this:
z = (x > 1) & (x < 2). The absence of NumPy operator forms oflogical_andandlogical_oris an unfortunate consequence of Python’s design.
So one can also use ~ for logical_not and | for logical_or.
