How to Perform Numpy\'s Logical OR on More Than Two Arrays?

Numpy logical_or for More Than Two Arguments

The logical_or function in Numpy typically operates on only two arrays. However, if you need to compute the union of more than two arrays, there are several approaches you can consider.

Chaining Logical Operators

One method involves chaining multiple logical_or calls like so:

x = np.array([True, True, False, False])
y = np.array([True, False, True, False])
z = np.array([False, False, False, False])
result = np.logical_or(np.logical_or(x, y), z)  # Union of arrays
print(result)  # Output: [ True  True  True False]

NumPy reduce Function

Another approach is to use the reduce function:

import numpy as np

# Union using reduce
result = np.logical_or.reduce((x, y, z))  # Union of arrays
print(result)  # Output: [ True  True  True False]

This method generalizes the chaining approach and can be used with multi-dimensional arrays as well.

Other Approaches

Beyond the aforementioned methods, you can also consider:

• Python's reduce function:

import functools

result = functools.reduce(np.logical_or, (x, y, z))  # Union of arrays
print(result)  # Output: [ True  True  True False]

• NumPy's any function with axis specification:

result = np.any((x, y, z), axis=0)  # Union of arrays
print(result)  # Output: [ True  True  True False]

Note for Logical_xor

For operations like logical exclusive or (logical_xor), NumPy does not provide an all/any-type function.

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