Bug fix using GroupBy.resample produces inconsistent behavior when calling it over empty df #47705

doc/source/whatsnew/v1.5.0.rst

@@ -1088,6 +1088,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.resample` reduction methods when used with ``on`` would attempt to aggregate the provided column (:issue:`47079`)
 - Bug in :meth:`DataFrame.groupby` and :meth:`Series.groupby` would not respect ``dropna=False`` when the input DataFrame/Series had a NaN values in a :class:`MultiIndex` (:issue:`46783`)
 - Bug in :meth:`DataFrameGroupBy.resample` raises ``KeyError`` when getting the result from a key list which misses the resample key (:issue:`47362`)
+- Bug in :meth:`DataFrameGroupBy.resample` produces inconsistent results when passing empty DataFrame (:issue:`47705`)
 -
 
 Reshaping

pandas/core/resample.py

@@ -514,11 +514,20 @@ def _wrap_result(self, result):
         """
         Potentially wrap any results.
         """
+        obj = self.obj
+        if (
+            isinstance(result, ABCDataFrame)
+            and result.empty
+            and not isinstance(result.index, PeriodIndex)
+        ):
+            result = result.set_index(
+                _asfreq_compat(obj.index[:0], freq=self.freq), append=True
+            )
+
         if isinstance(result, ABCSeries) and self._selection is not None:
             result.name = self._selection
 
         if isinstance(result, ABCSeries) and result.empty:
-            obj = self.obj
             # When index is all NaT, result is empty but index is not
             result.index = _asfreq_compat(obj.index[:0], freq=self.freq)
             result.name = getattr(obj, "name", None)

pandas/tests/resample/test_resampler_grouper.py

@@ -435,7 +435,11 @@ def test_empty(keys):
     # GH 26411
     df = DataFrame([], columns=["a", "b"], index=TimedeltaIndex([]))
     result = df.groupby(keys).resample(rule=pd.to_timedelta("00:00:01")).mean()
-    expected = DataFrame(columns=["a", "b"]).set_index(keys, drop=False)
+    expected = (
+        DataFrame(columns=["a", "b"])
+        .set_index(keys, drop=False)
+        .set_index(TimedeltaIndex([]), append=True)
+    )
     if len(keys) == 1:
         expected.index.name = keys[0]
 
@@ -497,3 +501,19 @@ def test_groupby_resample_with_list_of_keys():
         ),
     )
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("keys", [["a"], ["a", "b"]])
+def test_resample_empty_Dataframe(keys):
+    # GH 47705
+    df = DataFrame([], columns=["a", "b", "date"])
+    df["date"] = pd.to_datetime(df["date"])
+    df = df.set_index("date")
+    result = df.groupby(keys).resample(rule=pd.to_timedelta("00:00:01")).mean()
+    expected = DataFrame(columns=["a", "b", "date"]).set_index(keys, drop=False)
+    expected["date"] = pd.to_datetime(expected["date"])
+    expected = expected.set_index("date", append=True, drop=True)
+    if len(keys) == 1:
+        expected.index.name = keys[0]
+
+    tm.assert_frame_equal(result, expected)