BUG: Fix groupby.apply

doc/source/whatsnew/v1.0.0.rst

@@ -270,7 +270,7 @@ Plotting
 Groupby/resample/rolling
 ^^^^^^^^^^^^^^^^^^^^^^^^
 
--
+- Bug in :meth:`DataFrame.groupby.apply` only returning output from a single group in some cases (:issue:`28652`)
 - Bug in :meth:`DataFrame.rolling` not allowing for rolling over datetimes when ``axis=1`` (:issue: `28192`)
 - Bug in :meth:`DataFrame.groupby` not offering selection by column name when ``axis=1`` (:issue:`27614`)
 - Bug in :meth:`DataFrameGroupby.agg` not able to use lambda function with named aggregation (:issue:`27519`)

pandas/_libs/reduction.pyx

@@ -1,3 +1,4 @@
+from copy import copy
 from distutils.version import LooseVersion
 
 from cython import Py_ssize_t
@@ -16,6 +17,7 @@ cnp.import_array()
 
 cimport pandas._libs.util as util
 from pandas._libs.lib import maybe_convert_objects, values_from_object
+from pandas.api.types import is_scalar
 
 
 cdef _get_result_array(object obj, Py_ssize_t size, Py_ssize_t cnt):
@@ -543,7 +545,10 @@ def apply_frame_axis0(object frame, object f, object names,
                 # `piece` might not have an index, could be e.g. an int
                 pass
 
-            results.append(piece)
+            if is_scalar(piece):
+                results.append(piece)
+            else:
+                results.append(copy(piece))
 
             # If the data was modified inplace we need to
             # take the slow path to not risk segfaults

pandas/tests/frame/test_apply.py

@@ -1357,3 +1357,14 @@ def test_frequency_is_original(self, num_cols):
         df = DataFrame(1, index=index, columns=range(num_cols))
         df.apply(lambda x: x)
         assert index.freq == original.freq
+
+    def test_func_returns_object(self):
+        # GH 28652
+        df = DataFrame({"a": [1, 2]}, index=pd.Int64Index([1, 2]))
+        result = df.groupby("a").apply(lambda g: g.index)
+        expected = Series(
+            [pd.Int64Index([1]), pd.Int64Index([2])],
+            index=pd.Int64Index([1, 2], name="a"),
+        )
+
+        tm.assert_series_equal(result, expected)