parameters: (a, axis=None)
return_statement: return mask_rowcols(a, 0)
docstring: 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=999999)
      >>> 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=999999)
docstring_summary: Mask rows of a 2D array that contain masked values.
docstring_tokens: Mask rows of a 2D array that contain masked values .
function: def mask_rows(a, axis=None):
      """
      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=999999)
      >>> 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=999999)
  
      """
      return mask_rowcols(a, 0)
function_tokens: def mask_rows ( a , axis = None ) : return mask_rowcols ( a , 0 )