# numpy.ma.mask_rows

ma.mask_rows(a, axis=<no value>)

  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)

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