How to Efficiently Perform Multi-Array Union Operations with NumPy\'s `logical_or`?

Numpy logical_or for Multi-Array Union Operations

Numpy's logical_or function operates on pairs of arrays, leading to the question of how to efficiently combine multiple arrays for union operations (likewise for logical_and and intersections).

While logical_or itself accepts only two arguments, it can be chained together:

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)
# result: [ True,  True,  True,  False]

A more generalized approach involves using reduce:

result = np.logical_or.reduce((x, y, z))
# result: [ True,  True,  True,  False]

This method can be applied to both multi-dimensional arrays and tuples of 1D arrays. Additionally, Python's functools.reduce can be used in similar fashion:

result = functools.reduce(np.logical_or, (x, y, z))
# result: [ True,  True,  True,  False]

For convenience, Numpy provides any, which essentially performs a logical OR reduction along an axis:

result = np.any((x, y, z), axis=0)
# result: [ True,  True,  True,  False]

Similar principles apply to logical_and and other logical operators, except for logical_xor, which lacks a corresponding all/any-type function.

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