numpy.ma.nonzero

numpy.ma.nonzero(self)

Return the indices of unmasked elements that are not zero.

Returns a tuple of arrays, one for each dimension, containing the indices of the non-zero elements in that dimension. The corresponding non-zero values can be obtained with:

a[a.nonzero()]

To group the indices by element, rather than dimension, use instead:

np.transpose(a.nonzero())

The result of this is always a 2d array, with a row for each non-zero element.

Parameters:

None :

Returns:

tuple_of_arrays : tuple

Indices of elements that are non-zero.

See also

numpy.nonzero

Function operating on ndarrays.

flatnonzero

Return indices that are non-zero in the flattened version of the input array.

ndarray.nonzero

Equivalent ndarray method.

Examples

>>> import numpy.ma as ma
>>> x = ma.array(np.eye(3))
>>> x
masked_array(data =
 [[ 1.  0.  0.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]],
      mask =
 False,
      fill_value=1e+20)
>>> x.nonzero()
(array([0, 1, 2]), array([0, 1, 2]))

Masked elements are ignored.

>>> x[1, 1] = ma.masked
>>> x
masked_array(data =
 [[1.0 0.0 0.0]
 [0.0 -- 0.0]
 [0.0 0.0 1.0]],
      mask =
 [[False False False]
 [False  True False]
 [False False False]],
      fill_value=1e+20)
>>> x.nonzero()
(array([0, 2]), array([0, 2]))

Indices can also be grouped by element.

>>> np.transpose(x.nonzero())
array([[0, 0],
       [2, 2]])

A common use for nonzero is to find the indices of an array, where a condition is True. Given an array a, the condition a > 3 is a boolean array and since False is interpreted as 0, ma.nonzero(a > 3) yields the indices of the a where the condition is true.

>>> a = ma.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
masked_array(data =
 [[False False False]
 [ True  True  True]
 [ True  True  True]],
      mask =
 False,
      fill_value=999999)
>>> ma.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))

The nonzero method of the condition array can also be called.

>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))