Select elements of numpy array via boolean mask array

Question

I have a boolean mask array a of length n:

a = np.array([True, True, True, False, False])

I have a 2d array with n columns:

b = np.array([[1,2,3,4,5], [1,2,3,4,5]])

I want a new array which contains only the "True"-values, for example

c = ([[1,2,3], [1,2,3]])

c = a * b does not work because it contains also "0" for the false columns what I don't want

c = np.delete(b, a, 1) does not work

Any suggestions?

Answer 1

You probably want something like this:

>>> a = np.array([True, True, True, False, False])
>>> b = np.array([[1,2,3,4,5], [1,2,3,4,5]])
>>> b[:,a]
array([[1, 2, 3],
       [1, 2, 3]])

Note that for this kind of indexing to work, it needs to be an ndarray, like you were using, not a list, or it'll interpret the False and True as 0 and 1 and give you those columns:

>>> b[:,[True, True, True, False, False]]   
array([[2, 2, 2, 1, 1],
       [2, 2, 2, 1, 1]])