# numpy.ma.nonzero¶

`ma.``nonzero`(self) = <numpy.ma.core._frommethod object>

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_arraystuple

Indices of elements that are non-zero.

`numpy.nonzero`

Function operating on ndarrays.

`flatnonzero`

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

`numpy.ndarray.nonzero`

Equivalent ndarray method.

`count_nonzero`

Counts the number of non-zero elements in the input array.

Examples

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

```>>> x[1, 1] = ma.masked
>>> x
data=[[1.0, 0.0, 0.0],
[0.0, --, 0.0],
[0.0, 0.0, 1.0]],
[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
data=[[False, False, False],
[ True,  True,  True],
[ True,  True,  True]],
The `nonzero` method of the condition array can also be called.
```>>> (a > 3).nonzero()