I'm quite new to Python. I was reading and testing codes in the Numpy official documentation. At the end of part 2.6.2 (Indexing with Boolean Arrays) there's an example that returns a weird answer. Funny though, the documentation says " a weird thing to do".

a = np.arange(12).reshape(3, 4)
b1 = np.array([False, True, True])
b2 = np.array([True, False, True, False])
a[b1, b2]

I think it should return the following answer (or sth like that):

array([ 4, 6, 8, 10])

but it returns:

array([ 4, 10])

here is a logic map. Am I wrong somehow? Logic Map

CodePudding user response：

As the docs pointed out, it is a weird thing to do.

In practice, you would use different methods for each of the results that you want.

^{(see below)}

As @Michael pointed out in the comments, passing two boolean arrays converts them into index numbers first:

a[b1, b2]

 |
 V

a[np.flatnonzero(b1), np.flatnonzero(b2)]

 |
 V

a[(array([1, 2]), array([0, 2]))]

Perhaps it is implemented this way (and not like a[np.ix_(b1, b2)]) by the analogy of having only one array for indexing. For example, when you simply do a[b1], this might be happening internally: a[np.flatnonzero(b1)].

And for more than 1 array this is simply the same mechanic.

1. To get your expected output:

See this answer. In your case it would be:

a[b1][:, b2]

produces

array([[ 4,  6],
       [ 8, 10]])

which you can then .flatten():

array([ 4,  6,  8, 10])

Or:

You can use np.ix_:

np.ix_(b1, b2) produces a tuple of arrays:

(array([[1],
        [2]]),
 array([[0, 2]]))

which can be used to get the same result:

a[np.ix_(b1, b2)].flatten()

2. To get array([ 4, 10]) in a non-weird way:

Use integer array indexing explicitly:

a[[1, 2], [0, 2]]