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.