After fiddling around for a while, I figured things out, and am posting them here hoping it will help others.

Intuitively, np.where is like asking "tell me where in this array, entries satisfy a given condition".

>>> a = np.arange(5,10)
>>> np.where(a < 8)       # tell me where in a, entries are < 8
(array([0, 1, 2]),)       # answer: entries indexed by 0, 1, 2

It can also be used to get entries in array that satisfy the condition:

>>> a[np.where(a < 8)] 
array([5, 6, 7])          # selects from a entries 0, 1, 2

When a is a 2d array, np.where() returns an array of row idx's, and an array of col idx's:

>>> a = np.arange(4,10).reshape(2,3)
array([[4, 5, 6],
       [7, 8, 9]])
>>> np.where(a > 8)
(array(1), array(2))

As in the 1d case, we can use np.where() to get entries in the 2d array that satisfy the condition:

>>> a[np.where(a > 8)] # selects from a entries 0, 1, 2

array([9])

Note, when a is 1d, np.where() still returns an array of row idx's and an array of col idx's, but columns are of length 1, so latter is empty array.

You can use np.in1d:

np.where(np.in1d(letters, ['A', 'C']))[0]

Out[]: array([0, 2, 3, 5, 6, 8], dtype=int64)

If you've got multiple more complex conditions, you can use functools.reduce

from functools import reduce
conditions = [letters == 'A', letters == 'C']
np.where(reduce(np.logical_or, conditions))[0]

Out[]: array([0, 2, 3, 5, 6, 8], dtype=int64)