[BUG]: Groupy and Resample miscalculated aggregation

doc/source/whatsnew/v1.2.0.rst

@@ -314,6 +314,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrameGroupby.tshift` failing to raise ``ValueError`` when a frequency cannot be inferred for the index of a group (:issue:`35937`)
 - Bug in :meth:`DataFrame.groupby` does not always maintain column index name for ``any``, ``all``, ``bfill``, ``ffill``, ``shift`` (:issue:`29764`)
 - Bug in :meth:`DataFrameGroupBy.apply` raising error with ``np.nan`` group(s) when ``dropna=False`` (:issue:`35889`)
+- Bug when combining methods :meth:`DataFrame.groupby` with :meth:`DataFrame.resample` and restricting to `Series` or using `agg` did miscalculate the aggregation (:issue:`27343`, :issue:`33548`, :issue:`35275`).
 -
 
 Reshaping

pandas/core/groupby/grouper.py

@@ -338,6 +338,20 @@ def _set_grouper(self, obj: FrameOrSeries, sort: bool = False):
             if getattr(self.grouper, "name", None) == key and isinstance(
                 obj, ABCSeries
             ):
+                # In some cases the self._grouper may be sorted differently than obj.
+                # self.obj has the right order with the old index in the first go
+                # around. We align the index from obj with the self.obj index to
+                # select the correct values. Additionally we have to sort
+                # obj.index correctly to aggregate correctly.
+                if not hasattr(self, "_index_mapping"):
+                    self._index_mapping = DataFrame(
+                        self.obj.index, columns=["index"]
+                    ).sort_values(by="index")
+                obj.sort_index(inplace=True)
+                obj.index = self._index_mapping.loc[
+                    self._index_mapping["index"].isin(obj.index)
+                ].index
+                obj.sort_index(inplace=True)
                 ax = self._grouper.take(obj.index)
             else:
                 if key not in obj._info_axis:

pandas/tests/resample/test_resampler_grouper.py

@@ -347,3 +347,67 @@ def test_median_duplicate_columns():
     result = df.resample("5s").median()
     expected.columns = result.columns
     tm.assert_frame_equal(result, expected)
+
+
+def test_resample_different_result_with_agg():
+    # GH: 35275 and 33548
+    data = pd.DataFrame(
+        {
+            "cat": ["cat1", "cat1", "cat2", "cat1", "cat2", "cat1", "cat2", "cat1"],
+            "num": [5, 20, 22, 3, 4, 30, 10, 50],
+            "date": [
+                "2019-2-1",
+                "2018-02-03",
+                "2020-3-11",
+                "2019-2-2",
+                "2019-2-2",
+                "2018-12-4",
+                "2020-3-11",
+                "2020-12-12",
+            ],
+        }
+    )
+    data["date"] = pd.to_datetime(data["date"])
+
+    resampled = data.groupby("cat").resample("Y", on="date")
+
+    index = pd.MultiIndex.from_tuples(
+        [
+            ("cat1", "2018-12-31"),
+            ("cat1", "2019-12-31"),
+            ("cat1", "2020-12-31"),
+            ("cat2", "2019-12-31"),
+            ("cat2", "2020-12-31"),
+        ],
+        names=["cat", "date"],
+    )
+    index = index.set_levels([index.levels[0], pd.to_datetime(index.levels[1])])
+    expected = DataFrame([25, 4, 50, 4, 16], columns=pd.Index(["num"]), index=index)
+    result = resampled.agg({"num": "mean"})
+    tm.assert_frame_equal(result, expected)
+    result = resampled["num"].mean()
+    tm.assert_series_equal(result, expected["num"])
+    result = resampled.mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_resample_agg_different_results_on_keyword():
+    # GH: 27343
+    df = pd.DataFrame.from_records(
+        {
+            "ref": ["a", "a", "a", "b", "b"],
+            "time": [
+                "2014-12-31",
+                "2015-12-31",
+                "2016-12-31",
+                "2012-12-31",
+                "2014-12-31",
+            ],
+            "value": 5 * [1],
+        }
+    )
+    df["time"] = pd.to_datetime(df["time"])
+
+    expected = df.set_index("time").groupby("ref").resample(rule="M")["value"].sum()
+    result = df.groupby("ref").resample(rule="M", on="time")["value"].sum()
+    tm.assert_series_equal(result, expected)