parameters: (self)
return_statement: return zip(iter(self.index), iter(self))
docstring: Lazily iterate over (index, value) tuples.
  
          This method returns an iterable tuple (index, value). This is
          convenient if you want to create a lazy iterator.
  
          Returns
          -------
          iterable
              Iterable of tuples containing the (index, value) pairs from a
              Series.
  
          See Also
          --------
          DataFrame.items : Iterate over (column name, Series) pairs.
          DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
  
          Examples
          --------
          >>> s = pd.Series(['A', 'B', 'C'])
          >>> for index, value in s.items():
          ...     print(f"Index : {index}, Value : {value}")
          Index : 0, Value : A
          Index : 1, Value : B
          Index : 2, Value : C
docstring_summary: Lazily iterate over (index, value) tuples.
docstring_tokens: Lazily iterate over ( index value ) tuples .
function: def items(self):
          """
          Lazily iterate over (index, value) tuples.
  
          This method returns an iterable tuple (index, value). This is
          convenient if you want to create a lazy iterator.
  
          Returns
          -------
          iterable
              Iterable of tuples containing the (index, value) pairs from a
              Series.
  
          See Also
          --------
          DataFrame.items : Iterate over (column name, Series) pairs.
          DataFrame.iterrows : Iterate over DataFrame rows as (index, Series) pairs.
  
          Examples
          --------
          >>> s = pd.Series(['A', 'B', 'C'])
          >>> for index, value in s.items():
          ...     print(f"Index : {index}, Value : {value}")
          Index : 0, Value : A
          Index : 1, Value : B
          Index : 2, Value : C
          """
          return zip(iter(self.index), iter(self))
function_tokens: def items ( self ) : return zip ( iter ( self . index ) , iter ( self ) )