The prefered idiom for doing this is to use the function numpy.NONZERO() , or the nonzero() method of an array. Given an array a, the condition a > 3 RETURNS a boolean array and since False is INTERPRETED as 0 in Python and NumPy, NP.nonzero(a > 3)yields the indices of a where the condition is true.

>>> IMPORT numpy as np
>>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
array([[False, False, False],
[ True, True, True],
[ True, True, True]], dtype=bool)
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The nonzero() method of the boolean array can also be called.
>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

The prefered idiom for doing this is to use the function numpy.nonzero() , or the nonzero() method of an array. Given an array a, the condition a > 3 returns a boolean array and since False is interpreted as 0 in Python and NumPy, np.nonzero(a > 3)yields the indices of a where the condition is true.

>>> import numpy as np
>>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
array([[False, False, False],
[ True, True, True],
[ True, True, True]], dtype=bool)
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The nonzero() method of the boolean array can also be called.
>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))