have both a fastpath and a slowpath in groupby.agg

narwhals/pandas_like/group_by.py

@@ -159,7 +159,7 @@ def func(df: Any) -> Any:
 def agg_generic(  # noqa: PLR0913
     grouped: Any,
     exprs: list[Expr],
-    keys: list[str],
+    group_by_keys: list[str],
     output_names: list[str],
     implementation: str,
     from_dataframe: Callable[[Any], LazyFrame],
@@ -174,7 +174,7 @@ def agg_generic(  # noqa: PLR0913
 
     out: dict[str, list[Any]] = collections.defaultdict(list)
     for keys, df_keys in grouped:
-        for key, name in zip(keys, keys):
+        for key, name in zip(keys, group_by_keys):
             out[name].append(key)
         for expr in exprs:
             results_keys = expr._call(from_dataframe(df_keys))

tests/tpch_q1_test.py

@@ -1,7 +1,9 @@
 from __future__ import annotations
 
+import os
 from datetime import datetime
 from typing import Any
+from unittest import mock
 
 import polars
 import pytest
@@ -69,3 +71,64 @@ def test_q1(df_raw: Any) -> None:
         "count_order": [76, 1, 147, 71],
     }
     compare_dicts(result, expected)
+
+
+@pytest.mark.parametrize(
+    "df_raw",
+    [
+        (polars.read_parquet("tests/data/lineitem.parquet").to_pandas()),
+    ],
+)
+@mock.patch.dict(os.environ, {"NARWHALS_FORCE_GENERIC": "1"})
+def test_q1_w_pandas_agg_generic_path(df_raw: Any) -> None:
+    var_1 = datetime(1998, 9, 2)
+    df, pl = to_polars_api(df_raw, version="0.20")
+    query_result = (
+        df.filter(pl.col("l_shipdate") <= var_1)
+        .group_by(["l_returnflag", "l_linestatus"])
+        .agg(
+            [
+                pl.sum("l_quantity").alias("sum_qty"),
+                pl.sum("l_extendedprice").alias("sum_base_price"),
+                (pl.col("l_extendedprice") * (1 - pl.col("l_discount")))
+                .sum()
+                .alias("sum_disc_price"),
+                (
+                    pl.col("l_extendedprice")
+                    * (1.0 - pl.col("l_discount"))
+                    * (1.0 + pl.col("l_tax"))
+                )
+                .sum()
+                .alias("sum_charge"),
+                pl.mean("l_quantity").alias("avg_qty"),
+                pl.mean("l_extendedprice").alias("avg_price"),
+                pl.mean("l_discount").alias("avg_disc"),
+                pl.len().alias("count_order"),
+            ],
+        )
+        .sort(["l_returnflag", "l_linestatus"])
+    )
+    result = query_result.collect().to_dict(as_series=False)
+    expected = {
+        "l_returnflag": ["A", "N", "N", "R"],
+        "l_linestatus": ["F", "F", "O", "F"],
+        "sum_qty": [2109.0, 29.0, 3682.0, 1876.0],
+        "sum_base_price": [3114026.44, 39824.83, 5517101.99, 2947892.16],
+        "sum_disc_price": [2954950.8082, 39028.3334, 5205468.4852, 2816542.4816999994],
+        "sum_charge": [
+            3092840.4194289995,
+            39808.900068,
+            5406966.873419,
+            2935797.8313019997,
+        ],
+        "avg_qty": [27.75, 29.0, 25.047619047619047, 26.422535211267604],
+        "avg_price": [
+            40974.032105263155,
+            39824.83,
+            37531.30605442177,
+            41519.607887323946,
+        ],
+        "avg_disc": [0.05039473684210526, 0.02, 0.05537414965986395, 0.04507042253521127],
+        "count_order": [76, 1, 147, 71],
+    }
+    compare_dicts(result, expected)