diff --git a/doc/source/v0.15.0.txt b/doc/source/v0.15.0.txt
index e6c442159336b..16033dd75204c 100644
--- a/doc/source/v0.15.0.txt
+++ b/doc/source/v0.15.0.txt
@@ -162,7 +162,7 @@ previously results in ``Exception`` or ``TypeError`` (:issue:`7812`)
      didx
      didx.tz_localize(None)
 
-- ``DataFrame.tz_localize`` and ``DataFrame.tz_convert`` now accepts an optional ``level`` argument 
+- ``DataFrame.tz_localize`` and ``DataFrame.tz_convert`` now accepts an optional ``level`` argument
   for localizing a specific level of a MultiIndex (:issue:`7846`)
 
 .. _whatsnew_0150.refactoring:
@@ -302,6 +302,7 @@ Performance
 
 - Performance improvements in ``DatetimeIndex.__iter__`` to allow faster iteration (:issue:`7683`)
 - Performance improvements in ``Period`` creation (and ``PeriodIndex`` setitem) (:issue:`5155`)
+- Improvements in Series.transform for significant performance gains (revised) (:issue:`6496`)
 
 
 
diff --git a/pandas/core/groupby.py b/pandas/core/groupby.py
index 484d1d413c6c6..f26a7269772a3 100644
--- a/pandas/core/groupby.py
+++ b/pandas/core/groupby.py
@@ -2270,14 +2270,21 @@ def transform(self, func, *args, **kwargs):
         -------
         transformed : Series
         """
-        dtype = self._selected_obj.dtype
 
+        # if string function
         if isinstance(func, compat.string_types):
-            wrapper = lambda x: getattr(x, func)(*args, **kwargs)
-        else:
-            wrapper = lambda x: func(x, *args, **kwargs)
+            return self._transform_fast(lambda : getattr(self, func)(*args, **kwargs))
+
+        # do we have a cython function
+        cyfunc = _intercept_cython(func)
+        if cyfunc and not args and not kwargs:
+            return self._transform_fast(cyfunc)
 
+        # reg transform
+        dtype = self._selected_obj.dtype
         result = self._selected_obj.values.copy()
+
+        wrapper = lambda x: func(x, *args, **kwargs)
         for i, (name, group) in enumerate(self):
 
             object.__setattr__(group, 'name', name)
@@ -2302,6 +2309,29 @@ def transform(self, func, *args, **kwargs):
                                             index=self._selected_obj.index,
                                             name=self._selected_obj.name)
 
+    def _transform_fast(self, func):
+        """
+        fast version of transform, only applicable to builtin/cythonizable functions
+        """
+        if isinstance(func, compat.string_types):
+            func = getattr(self,func)
+        values = func().values
+        counts = self.count().values
+        values = np.repeat(values, counts)
+
+        # the values/counts are repeated according to the group index
+        indices = self.indices
+
+        # shortcut of we have an already ordered grouper
+        if Index(self.grouper.group_info[0]).is_monotonic:
+            result = Series(values, index=self.obj.index)
+        else:
+            index = Index(np.concatenate([ indices[v] for v in self.grouper.result_index ]))
+            result = Series(values, index=index).sort_index()
+            result.index = self.obj.index
+
+        return result
+
     def filter(self, func, dropna=True, *args, **kwargs):
         """
         Return a copy of a Series excluding elements from groups that
diff --git a/pandas/tests/test_groupby.py b/pandas/tests/test_groupby.py
index c88ba836886bf..f621b0fb94eaf 100644
--- a/pandas/tests/test_groupby.py
+++ b/pandas/tests/test_groupby.py
@@ -795,6 +795,20 @@ def test_transform(self):
         transformed = grouped.transform(lambda x: x * x.sum())
         self.assertEqual(transformed[7], 12)
 
+    def test_transform_fast(self):
+
+        df = DataFrame( { 'id' : np.arange( 100000 ) / 3,
+                          'val': np.random.randn( 100000) } )
+
+        grp=df.groupby('id')['val']
+
+        expected = pd.Series(np.repeat(grp.mean().values, grp.count().values),index=df.index)
+        result = grp.transform(np.mean)
+        assert_series_equal(result,expected)
+
+        result = grp.transform('mean')
+        assert_series_equal(result,expected)
+
     def test_transform_broadcast(self):
         grouped = self.ts.groupby(lambda x: x.month)
         result = grouped.transform(np.mean)
@@ -858,12 +872,14 @@ def test_transform_select_columns(self):
         assert_frame_equal(result, expected)
 
     def test_transform_exclude_nuisance(self):
+
+        # this also tests orderings in transform between
+        # series/frame to make sure its consistent
         expected = {}
         grouped = self.df.groupby('A')
         expected['C'] = grouped['C'].transform(np.mean)
         expected['D'] = grouped['D'].transform(np.mean)
         expected = DataFrame(expected)
-
         result = self.df.groupby('A').transform(np.mean)
 
         assert_frame_equal(result, expected)
diff --git a/vb_suite/groupby.py b/vb_suite/groupby.py
index 9f520df122c2f..788f228c81edd 100644
--- a/vb_suite/groupby.py
+++ b/vb_suite/groupby.py
@@ -444,5 +444,13 @@ def f(g):
 
 df = DataFrame({ 'signal' : np.random.rand(N)})
 """
-
 groupby_transform_series = Benchmark("df['signal'].groupby(g).transform(np.mean)", setup)
+
+setup = common_setup + """
+np.random.seed(0)
+
+df=DataFrame( { 'id' : np.arange( 100000 ) / 3,
+                'val': np.random.randn( 100000) } )
+"""
+
+groupby_transform_series2 = Benchmark("df.groupby('id')['val'].transform(np.mean)", setup)