numpy get index where value is true

To get the row numbers where at least one item is larger than 15:

>>> np.where(np.any(e>15, axis=1))(array([1, 2], dtype=int64),)

You can use the nonzero function. it returns the nonzero indices of the given input.

Easy Way

>>> (e > 15).nonzero()(array([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), array([6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))

to see the indices more cleaner, use transpose method:

>>> numpy.transpose((e>15).nonzero())[[1 6] [1 7] [1 8] [1 9] [2 0] ...

Not Bad Way

>>> numpy.nonzero(e > 15)(array([1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), array([6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9]))

or the clean way:

>>> numpy.transpose(numpy.nonzero(e > 15))[[1 6] [1 7] [1 8] [1 9] [2 0] ...

A simple and clean way: use np.argwhere to group the indices by element, rather than dimension as in np.nonzero(a) (i.e., np.argwhere returns a row for each non-zero element).

>>> a = np.arange(10)>>> aarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])>>> np.argwhere(a>4)array([[5],       [6],       [7],       [8],       [9]])

np.argwhere(a) is almost the same as np.transpose(np.nonzero(a)), but it produces a result of the correct shape for a 0-d array.

Note: You cannot use a(np.argwhere(a>4)) to get the corresponding values in a. The recommended way is to use a[(a>4).astype(bool)] or a[(a>4) != 0] rather than a[np.nonzero(a>4)] as they handle 0-d arrays correctly. See the documentation for more details. As can be seen in the following example, a[(a>4).astype(bool)] and a[(a>4) != 0] can be simplified to a[a>4].

Another example:

>>> a = np.array([5,-15,-8,-5,10])>>> aarray([  5, -15,  -8,  -5,  10])>>> a > 4array([ True, False, False, False,  True])>>> a[a > 4]array([ 5, 10])>>> a = np.add.outer(a,a)>>> aarray([[ 10, -10,  -3,   0,  15],       [-10, -30, -23, -20,  -5],       [ -3, -23, -16, -13,   2],       [  0, -20, -13, -10,   5],       [ 15,  -5,   2,   5,  20]])>>> a = np.argwhere(a>4)>>> aarray([[0, 0],       [0, 4],       [3, 4],       [4, 0],       [4, 3],       [4, 4]])>>> for i,j in a: print(i,j)... 0 00 43 44 04 34 4>>>