parameters: (a, axis=np._NoValue)
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=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)
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=np._NoValue):
      """
      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)
  
      """
      if axis is not np._NoValue:
          # remove the axis argument when this deprecation expires
          # NumPy 1.18.0, 2019-11-28
          warnings.warn(
              "The axis argument has always been ignored, in future passing it "
              "will raise TypeError", DeprecationWarning, stacklevel=2)
      return mask_rowcols(a, 0)
function_tokens: def mask_rows ( a , axis = np . _NoValue ) : if axis is not np . _NoValue : # remove the axis argument when this deprecation expires # NumPy 1.18.0, 2019-11-28 warnings . warn ( "The axis argument has always been ignored, in future passing it " "will raise TypeError" , DeprecationWarning , stacklevel = 2 ) return mask_rowcols ( a , 0 )