CLN: no need to catch libgroupby validation ValueError (#41240)

pandas/core/resample.py

@@ -430,16 +430,23 @@ 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, AttributeError, KeyError):
+        except DataError:
+            # got TypeErrors on aggregation
+            result = grouped.apply(how, *args, **kwargs)
+        except (AttributeError, KeyError):
             # we have a non-reducing function; try to evaluate
             # alternatively we want to evaluate only a column of the input
+
+            # test_apply_to_one_column_of_df the function being applied references
+            #  a DataFrame column, but aggregate_item_by_item operates column-wise
+            #  on Series, raising AttributeError or KeyError
+            #  (depending on whether the column lookup uses getattr/__getitem__)
             result = grouped.apply(how, *args, **kwargs)
+
         except ValueError as err:
             if "Must produce aggregated value" in str(err):
                 # raised in _aggregate_named
-                pass
-            elif "len(index) != len(labels)" in str(err):
-                # raised in libgroupby validation
+                # see test_apply_without_aggregation, test_apply_with_mutated_index
                 pass
             else:
                 raise

pandas/tests/resample/test_resampler_grouper.py

@@ -354,11 +354,15 @@ def test_apply_to_one_column_of_df():
         {"col": range(10), "col1": range(10, 20)},
         index=date_range("2012-01-01", periods=10, freq="20min"),
     )
+
+    # access "col" via getattr -> make sure we handle AttributeError
     result = df.resample("H").apply(lambda group: group.col.sum())
     expected = Series(
         [3, 12, 21, 9], index=date_range("2012-01-01", periods=4, freq="H")
     )
     tm.assert_series_equal(result, expected)
+
+    # access "col" via _getitem__ -> make sure we handle KeyErrpr
     result = df.resample("H").apply(lambda group: group["col"].sum())
     tm.assert_series_equal(result, expected)