NumPy参考 >数组对象 >Masked array operations >numpy.ma.is_mask > numpy.ma.mask_rows
numpy.ma.
mask_rows
(a, axis=<no value>)[source]¶Mask rows of a 2D array that contain masked values.
This function is a shortcut to mask_rowcols
with axis equal to 0.
See also
mask_rowcols
Mask rows and/or columns of a 2D array.
masked_where
Mask where a condition is met.
Examples
>>> import numpy.ma as ma
>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
[0, 1, 0],
[0, 0, 0]])
>>> a = ma.masked_equal(a, 1)
>>> a
masked_array(
data=[[0, 0, 0],
[0, --, 0],
[0, 0, 0]],
mask=[[False, False, False],
[False, True, False],
[False, False, False]],
fill_value=1)
>>> ma.mask_rows(a)
masked_array(
data=[[0, 0, 0],
[--, --, --],
[0, 0, 0]],
mask=[[False, False, False],
[ True, True, True],
[False, False, False]],
fill_value=1)