BUG: GH2893 Produce a series on apply if we have a singular series and have a simple index

RELEASE.rst

@@ -262,6 +262,8 @@ pandas 0.11.0
     with an indexer (GH3216_)
   - Invoking df.applymap on a dataframe with dupe cols now raises a ValueError (GH2786_)
   - Apply with invalid returned indices raise correct Exception (GH2808_)
+  - Produce a series on apply if we only generate a singular series and have
+    a simple index (GH2893_)
 
 .. _GH622: https://github.com/pydata/pandas/issues/622
 .. _GH797: https://github.com/pydata/pandas/issues/797
@@ -313,6 +315,7 @@ pandas 0.11.0
 .. _GH2850: https://github.com/pydata/pandas/issues/2850
 .. _GH2898: https://github.com/pydata/pandas/issues/2898
 .. _GH2892: https://github.com/pydata/pandas/issues/2892
+.. _GH2893: https://github.com/pydata/pandas/issues/2893
 .. _GH2902: https://github.com/pydata/pandas/issues/2902
 .. _GH2903: https://github.com/pydata/pandas/issues/2903
 .. _GH2909: https://github.com/pydata/pandas/issues/2909

pandas/core/groupby.py

@@ -1834,11 +1834,23 @@ def _wrap_applied_output(self, keys, values, not_indexed_same=False):
                     key_index = Index(keys, name=key_names[0])
 
             if isinstance(values[0], np.ndarray):
-                if (isinstance(values[0], Series) and
-                        not _all_indexes_same([x.index for x in values])):
-                    return self._concat_objects(keys, values,
-                                                not_indexed_same=not_indexed_same)
-
+                if isinstance(values[0], Series):
+                    applied_index    = self.obj._get_axis(self.axis)
+                    all_indexed_same = _all_indexes_same([x.index for x in values])
+                    singular_series  = len(values) == 1 and applied_index.nlevels == 1
+
+                    # assign the name to this series
+                    if singular_series:
+                        values[0].name = keys[0]
+
+                    # GH2893
+                    # we have series in the values array, we want to produce a series:
+                    # if any of the sub-series are not indexed the same
+                    # OR we don't have a multi-index and we have only a single values
+                    if singular_series or not all_indexed_same:
+                        return self._concat_objects(keys, values,
+                                                    not_indexed_same=not_indexed_same)
+
                 try:
                     if self.axis == 0:
 

pandas/tests/test_groupby.py

@@ -261,6 +261,26 @@ def test_groupby_nonobject_dtype(self):
         expected = self.mframe.groupby(key.astype('O')).sum()
         assert_frame_equal(result, expected)
 
+    def test_groupby_return_type(self):
+
+        # GH2893
+        df1 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, 
+                         {"val1":2, "val2": 27}, {"val1":2, "val2": 12}])
+
+        def func(dataf):
+            return dataf["val2"]  - dataf["val2"].mean()
+
+        result = df1.groupby("val1").apply(func)
+        self.assert_(isinstance(result,Series))
+
+        df2 = DataFrame([{"val1": 1, "val2" : 20}, {"val1":1, "val2": 19}, 
+                         {"val1":1, "val2": 27}, {"val1":1, "val2": 12}])
+        def func(dataf):
+            return dataf["val2"]  - dataf["val2"].mean()
+
+        result = df2.groupby("val1").apply(func)
+        self.assert_(isinstance(result,Series))
+
     def test_agg_regression1(self):
         grouped = self.tsframe.groupby([lambda x: x.year, lambda x: x.month])
         result = grouped.agg(np.mean)