code: def map(self, mapper):
          
          # this is used in apply.
          # We get hit since we're an "is_extension_type" but regular extension
          # types are not hit. This may be worth adding to the interface.
          if isinstance(mapper, ABCSeries):
              mapper = mapper.to_dict()
  
          if isinstance(mapper, abc.Mapping):
              fill_value = mapper.get(self.fill_value, self.fill_value)
              sp_values = [mapper.get(x, None) for x in self.sp_values]
          else:
              fill_value = mapper(self.fill_value)
              sp_values = [mapper(x) for x in self.sp_values]
  
          return type(self)(sp_values, sparse_index=self.sp_index,
                            fill_value=fill_value)
docs: Map categories using input correspondence (dict, Series, or function).
  
          Parameters
          ----------
          mapper : dict, Series, callable
              The correspondence from old values to new.
  
          Returns
          -------
          SparseArray
              The output array will have the same density as the input.
              The output fill value will be the result of applying the
              mapping to ``self.fill_value``
  
          Examples
          --------
          >>> arr = pd.SparseArray([0, 1, 2])
          >>> arr.apply(lambda x: x + 10)
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)
  
          >>> arr.apply({0: 10, 1: 11, 2: 12})
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)
  
          >>> arr.apply(pd.Series([10, 11, 12], index=[0, 1, 2]))
          [10, 11, 12]
          Fill: 10
          IntIndex
          Indices: array([1, 2], dtype=int32)