fixup slow transforms

Author: jreback

pandas/core/groupby.py

@@ -2890,39 +2890,32 @@ def transform(self, func, *args, **kwargs):
                     lambda: getattr(self, func)(*args, **kwargs))
 
         # reg transform
-        dtype = self._selected_obj.dtype
-        result = self._selected_obj.values.copy()
-
+        klass = self._selected_obj.__class__
+        results = []
         wrapper = lambda x: func(x, *args, **kwargs)
-        for i, (name, group) in enumerate(self):
+        for name, group in self:
             object.__setattr__(group, 'name', name)
             res = wrapper(group)
 
             if hasattr(res, 'values'):
                 res = res.values
 
-            # may need to astype
-            try:
-                common_type = np.common_type(np.array(res), result)
-                if common_type != result.dtype:
-                    result = result.astype(common_type)
-            except Exception as exc:
-                # date math can cause type of result to change
-                if i == 0 and (is_datetime64_dtype(result.dtype) or
-                               is_timedelta64_dtype(result.dtype)):
-                    try:
-                        dtype = res.dtype
-                    except Exception as exc:
-                        dtype = type(res)
-                    result = np.empty_like(result, dtype)
-
             indexer = self._get_index(name)
-            result[indexer] = res
+            s = klass(res, indexer)
+            results.append(s)
 
-        result = _possibly_downcast_to_dtype(result, dtype)
-        return self._selected_obj.__class__(result,
-                                            index=self._selected_obj.index,
-                                            name=self._selected_obj.name)
+        from pandas.tools.concat import concat
+        result = concat(results).sort_index()
+
+        # we will only try to coerce the result type if
+        # we have a numeric dtype
+        dtype = self._selected_obj.dtype
+        if is_numeric_dtype(dtype):
+            result = _possibly_downcast_to_dtype(result, dtype)
+
+        result.name = self._selected_obj.name
+        result.index = self._selected_obj.index
+        return result
 
     def _transform_fast(self, func):
         """

pandas/tests/groupby/test_filters.py

@@ -216,6 +216,7 @@ def test_filter_against_workaround(self):
         grouper = s.apply(lambda x: np.round(x, -1))
         grouped = s.groupby(grouper)
         f = lambda x: x.mean() > 10
+
         old_way = s[grouped.transform(f).astype('bool')]
         new_way = grouped.filter(f)
         assert_series_equal(new_way.sort_values(), old_way.sort_values())