NumPy参考 >数组对象 >Masked array operations > numpy.ma.nonzero
numpy.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.
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.
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
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=True)
>>> 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]))