Are there builtin functions for elementwise boolean operators over boolean lists?

Question:

For example, if you have n lists of bools of the same length, then elementwise boolean AND should return another list of that length that has True in those positions where all the input lists have True, and False everywhere else.

It’s pretty easy to write, i just would prefer to use a builtin if one exists (for the sake of standardization/readability).

Here’s an implementation of elementwise AND:

def eAnd(*args):
    return [all(tuple) for tuple in zip(*args)]

example usage:

>>> eAnd([True, False, True, False, True], [True, True, False, False, True], [True, True, False, False, True])
[True, False, False, False, True]
Asked By: bshanks

||
Source

Answers:

No, there are no such built-ins. Your method using zip and all / any is what I would use.

Answered By: Eli Bendersky

No, I don’t believe there’s any such function in the standard library… especially when it’s so easy to write in terms of the functions that are provided.

Answered By: David Z

There is not a built-in way to do this. Generally speaking, list comprehensions and the like are how you do elementwise operations in Python.

Numpy does provide this (using &, for technical limitations) in its array type. Numpy arrays usually perform operations elementwise.

Answered By: Mike Graham

The numpy.all function does what you want, if you specify the dimension to collapse on:

>>> all([[True, False, True, False, True], [True, True, False, False, True], [True, True, False, False, True]], 0)
array([ True, False, False, False,  True], dtype=bool)
Answered By: Tom

Try:

[ x&y for (x,y) in zip(list_a, list_b)]

If you are dealing with really long lists, or
some of your variables are / need to be numpy arrays, the equivalent numpy code would be:

list( np.array(list_a) & np.array(list_b) )

modify it based on your needs.

Answered By: ntg