BUG: Regression in Resample.apply raised error when apply affected on…

…ly a Series (#37198)

doc/source/whatsnew/v1.1.4.rst

@@ -20,6 +20,7 @@ Fixed regressions
 - Fixed regression in :class:`RollingGroupby` with ``sort=False`` not being respected (:issue:`36889`)
 - Fixed regression in :meth:`Series.astype` converting ``None`` to ``"nan"`` when casting to string (:issue:`36904`)
 - Fixed regression in :class:`RollingGroupby` causing a segmentation fault with Index of dtype object (:issue:`36727`)
+- Fixed regression in :meth:`DataFrame.resample(...).apply(...)` raised ``AttributeError`` when input was a :class:`DataFrame` and only a :class:`Series` was evaluated (:issue:`36951`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/resample.py

@@ -369,8 +369,9 @@ def _groupby_and_aggregate(self, how, grouper=None, *args, **kwargs):
                 result = grouped._aggregate_item_by_item(how, *args, **kwargs)
             else:
                 result = grouped.aggregate(how, *args, **kwargs)
-        except DataError:
+        except (DataError, AttributeError, KeyError):
             # we have a non-reducing function; try to evaluate
+            # alternatively we want to evaluate only a column of the input
             result = grouped.apply(how, *args, **kwargs)
         except ValueError as err:
             if "Must produce aggregated value" in str(err):

pandas/tests/resample/test_resampler_grouper.py

@@ -347,3 +347,18 @@ def test_median_duplicate_columns():
     result = df.resample("5s").median()
     expected.columns = result.columns
     tm.assert_frame_equal(result, expected)
+
+
+def test_apply_to_one_column_of_df():
+    # GH: 36951
+    df = pd.DataFrame(
+        {"col": range(10), "col1": range(10, 20)},
+        index=pd.date_range("2012-01-01", periods=10, freq="20min"),
+    )
+    result = df.resample("H").apply(lambda group: group.col.sum())
+    expected = pd.Series(
+        [3, 12, 21, 9], index=pd.date_range("2012-01-01", periods=4, freq="H")
+    )
+    tm.assert_series_equal(result, expected)
+    result = df.resample("H").apply(lambda group: group["col"].sum())
+    tm.assert_series_equal(result, expected)