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# numpy.ma.mask_rowcols

ma.mask_rowcols(a, axis=None)

  Mask rows and/or columns of a 2D array that contain masked values.

  Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter.

  > - If axis is None, rows and columns are masked.

  > - If axis is 0, only rows are masked.

  > - If axis is 1 or -1, only columns are masked.

  Parameters:
    a array_like, MaskedArray

      The array to mask. If not a MaskedArray instance (or if no array elements are masked), the result is a MaskedArray with mask set to `nomask` (False). Must be a 2D array.

    axis int, optional

      Axis along which to perform the operation. If None, applies to a flattened version of the array.

  Returns:
    a MaskedArray

      A modified version of the input array, masked depending on the value of the axis parameter.

  Raises:
    NotImplementedError

      If input array a is not 2D.

  See also

  `mask_rows`

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

  `mask_cols`

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

  `masked_where`

    Mask where a condition is met.

  Notes

  The input array’s mask is modified by this function.

  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_rowcols(a)
      masked_array(
        data=[[0, --, 0],
              [--, --, --],
              [0, --, 0]],
        mask=[[False,  True, False],
              [ True,  True,  True],
              [False,  True, False]],
        fill_value=1)