fix: groupby aggregates no longer check if grouping keys are numeric

bigframes/core/groupby/__init__.py

@@ -358,8 +358,8 @@ def _convert_index(self, dataframe: df.DataFrame):
 
     def _raise_on_non_numeric(self, op: str):
         if not all(
-            dtype in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE
-            for dtype in self._block.dtypes
+            self._column_type(col) in dtypes.NUMERIC_BIGFRAMES_TYPES_PERMISSIVE
+            for col in self._selected_cols
         ):
             raise NotImplementedError(
                 f"'{op}' does not support non-numeric columns. "

bigframes/dataframe.py

@@ -2337,6 +2337,7 @@ def groupby(
             blocks.Label,
             bigframes.series.Series,
             typing.Sequence[typing.Union[blocks.Label, bigframes.series.Series]],
+            None,
         ] = None,
         *,
         level: typing.Optional[LevelsType] = None,

tests/system/small/test_groupby.py

@@ -371,3 +371,20 @@ def test_series_groupby_agg_list(scalars_df_index, scalars_pandas_df_index):
     pd.testing.assert_frame_equal(
         pd_result, bf_result_computed, check_dtype=False, check_names=False
     )
+
+
+def test_dataframe_groupby_nonnumeric_with_mean():
+    df = pd.DataFrame(
+        {
+            "key1": ["a", "a", "a", "b"],
+            "key2": ["a", "a", "c", "c"],
+            "key3": [1, 2, 3, 4],
+            "key4": [1.6, 2, 3, 4],
+        }
+    )
+    pd_result = df.groupby(["key1", "key2"]).mean()
+    bf_result = bpd.DataFrame(df).groupby(["key1", "key2"]).mean().to_pandas()
+
+    pd.testing.assert_frame_equal(
+        pd_result, bf_result, check_index_type=False, check_dtype=False
+    )