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# numpy.ma.mask_rowcols

`numpy.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.ma as ma
      >>> a = np.zeros((3, 3), dtype=np.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=999999)
      >>> ma.mask_rowcols(a)
      masked_array(data =
       [[0 -- 0]
       [-- -- --]
       [0 -- 0]],
            mask =
       [[False  True False]
       [ True  True  True]
       [False  True False]],
            fill_value=999999)