Address changes requested by @jreback

Author: rtlee9

asv_bench/benchmarks/series_methods.py

@@ -157,9 +157,11 @@ def setup(self, mapper, dtype):
             self.map_data = map_data
         elif mapper == 'dict':
             self.map_data = map_data.to_dict()
-        else:
+        elif mapper == 'lambda':
             map_dict = map_data.to_dict()
             self.map_data = lambda x: map_dict[x]
+        else:
+            raise NotImplementedError
 
         self.s = Series(np.random.randint(0, map_size, 10000), dtype=dtype)
 

doc/source/whatsnew/v0.25.0.rst

@@ -248,7 +248,7 @@ Performance Improvements
 - Improved performance of :meth:`pandas.core.groupby.GroupBy.quantile` (:issue:`20405`)
 - Improved performance of :meth:`read_csv` by faster tokenizing and faster parsing of small float numbers (:issue:`25784`)
 - Improved performance of :meth:`read_csv` by faster parsing of N/A and boolean values (:issue:`25804`)
-- Improved performance of :meth:`pandas.core.base.IndexOpsMixin._map_values` for dictionary mappers on categorical series by using the default series map method (:issue:`23785`)
+- Improved performance of :meth:`Series.map` for dictionary mappers on categorical series by mapping the categories instead of mapping all values (:issue:`23785`)
 
 .. _whatsnew_0250.bug_fixes:
 

pandas/core/base.py

@@ -1196,16 +1196,15 @@ def _map_values(self, mapper, na_action=None):
         if isinstance(mapper, ABCSeries):
             # Since values were input this means we came from either
             # a dict or a series and mapper should be an index
+            if is_categorical_dtype(self._values):
+                # use the built in categorical series mapper which saves
+                # time by mapping the categories instead of all values
+                return self._values.map(mapper)
             if is_extension_type(self.dtype):
                 values = self._values
             else:
                 values = self.values
 
-            if is_categorical_dtype(values):
-                # use the built in categorical series mapper which saves
-                # time by mapping the categories instead of all values
-                return values.map(mapper)
-
             indexer = mapper.index.get_indexer(values)
             new_values = algorithms.take_1d(mapper._values, indexer)