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# numpy.ma.mask_cols

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

  Mask columns of a 2D array that contain masked values.

  This function is a shortcut to `mask_rowcols` with axis equal to 1.

  See also

  `mask_rowcols`

    Mask rows and/or columns of a 2D array.

  `masked_where`

    Mask where a condition is met.

  Examples

      >>> import numpy as np
      >>> a = np.zeros((3, 3), dtype=np.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_cols(a)
      masked_array(
        data=[[0, --, 0],
              [0, --, 0],
              [0, --, 0]],
        mask=[[False,  True, False],
              [False,  True, False],
              [False,  True, False]],
        fill_value=1)