fix: DataFrameGroupby.agg now works with unnamed tuples (#985)

Author: TrevorBergeron

bigframes/core/groupby/__init__.py

@@ -414,12 +414,10 @@ def _agg_named(self, **kwargs) -> df.DataFrame:
  raise NotImplementedError(
  f"Only string aggregate names supported. {constants.FEEDBACK_LINK}"
  )
- if not hasattr(v, "column") or not hasattr(v, "aggfunc"):
- import bigframes.pandas as bpd
-
- raise TypeError(f"kwargs values must be {bpd.NamedAgg.__qualname__}")
- col_id = self._resolve_label(v.column)
- aggregations.append((col_id, agg_ops.lookup_agg_func(v.aggfunc)))
+ if not isinstance(v, tuple) or (len(v) != 2):
+ raise TypeError("kwargs values must be 2-tuples of column, aggfunc")
+ col_id = self._resolve_label(v[0])
+ aggregations.append((col_id, agg_ops.lookup_agg_func(v[1])))
  column_labels.append(k)
  agg_block, _ = self._block.aggregate(
  by_column_ids=self._by_col_ids,

tests/system/small/test_groupby.py

@@ -247,6 +247,41 @@ def test_dataframe_groupby_agg_named(scalars_df_index, scalars_pandas_df_index):
  pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)
 
 
+def test_dataframe_groupby_agg_kw_tuples(scalars_df_index, scalars_pandas_df_index):
+ col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"]
+ bf_result = (
+ scalars_df_index[col_names]
+ .groupby("string_col")
+ .agg(
+ agg1=("int64_too", "sum"),
+ agg2=("float64_col", "max"),
+ )
+ )
+ pd_result = (
+ scalars_pandas_df_index[col_names]
+ .groupby("string_col")
+ .agg(agg1=("int64_too", "sum"), agg2=("float64_col", "max"))
+ )
+ bf_result_computed = bf_result.to_pandas()
+
+ pd.testing.assert_frame_equal(pd_result, bf_result_computed, check_dtype=False)
+
+
+@pytest.mark.parametrize(
+ ("kwargs"),
+ [
+ ({"hello": "world"}),
+ ({"too_many_fields": ("one", "two", "three")}),
+ ],
+)
+def test_dataframe_groupby_agg_kw_error(scalars_df_index, kwargs):
+ col_names = ["int64_too", "float64_col", "int64_col", "bool_col", "string_col"]
+ with pytest.raises(
+ TypeError, match=r"kwargs values must be 2-tuples of column, aggfunc"
+ ):
+ (scalars_df_index[col_names].groupby("string_col").agg(**kwargs))
+
+
 @pytest.mark.parametrize(
  ("as_index"),
  [