numpy.ma.mask_rows#

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

>>> a = np.zeros((3, 3), dtype=int)
>>> a[1, 1] = 1
>>> a
array([[0, 0, 0],
       [0, 1, 0],
       [0, 0, 0]])
>>> a = np.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)

>>> np.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)