More specific types for GroupBy.apply.

pandas-stubs/core/groupby/generic.pyi

@@ -100,6 +100,7 @@ class SeriesGroupBy(GroupBy):
     def nlargest(self, n: int = ..., keep: str = ...) -> Series[S1]: ...
     def nsmallest(self, n: int = ..., keep: str = ...) -> Series[S1]: ...
     def nth(self, n: int | Sequence[int], dropna: str | None = ...) -> Series[S1]: ...
+    def sum(self, **kwargs) -> Series[S1]: ...
 
 class _DataFrameGroupByScalar(DataFrameGroupBy):
     def __iter__(self) -> Iterator[tuple[Scalar, DataFrame]]: ...
@@ -110,17 +111,19 @@ class _DataFrameGroupByNonScalar(DataFrameGroupBy):
 class DataFrameGroupBy(GroupBy):
     def any(self, skipna: bool = ...) -> DataFrame: ...
     def all(self, skipna: bool = ...) -> DataFrame: ...
-    # mypy sees the two overloads as overlapping
+    # mypy and pyright see these overloads as overlapping
     @overload
     def apply(  # type: ignore[misc]
-        self, func: Callable[[DataFrame], Series | Scalar], *args, **kwargs
+        self, func: Callable[[DataFrame], Scalar | list | dict], *args, **kwargs
     ) -> Series: ...
     @overload
     def apply(  # type: ignore[misc]
-        self, func: Callable[[Iterable], Series | Scalar], *args, **kwargs
+        self, func: Callable[[DataFrame], Series | DataFrame], *args, **kwargs
     ) -> DataFrame: ...
     @overload
-    def apply(self, func: Callable, *args, **kwargs) -> DataFrame | Series: ...
+    def apply(  # type: ignore[misc]
+        self, func: Callable[[Iterable], float], *args, **kwargs
+    ) -> DataFrame: ...
     @overload
     def aggregate(self, arg: str, *args, **kwargs) -> DataFrame: ...
     @overload

tests/test_frame.py

@@ -1346,13 +1346,32 @@ def test_groupby_apply() -> None:
     # GH 167
     df = pd.DataFrame({"col1": [1, 2, 3], "col2": [4, 5, 6]})
 
-    def summean(x: pd.DataFrame) -> float:
+    def sum_mean(x: pd.DataFrame) -> float:
         return x.sum().mean()
 
-    check(assert_type(df.groupby("col1").apply(summean), pd.Series), pd.Series)
+    check(assert_type(df.groupby("col1").apply(sum_mean), pd.Series), pd.Series)
 
     lfunc: Callable[[pd.DataFrame], float] = lambda x: x.sum().mean()
     check(
         assert_type(df.groupby("col1").apply(lfunc), pd.Series),
         pd.Series,
     )
+
+    def sum_to_list(x: pd.DataFrame) -> list:
+        return x.sum().tolist()
+
+    check(assert_type(df.groupby("col1").apply(sum_to_list), pd.Series), pd.Series)
+
+    def sum_to_series(x: pd.DataFrame) -> pd.Series:
+        return x.sum()
+
+    check(
+        assert_type(df.groupby("col1").apply(sum_to_series), pd.DataFrame), pd.DataFrame
+    )
+
+    def sample_to_df(x: pd.DataFrame) -> pd.DataFrame:
+        return x.sample()
+
+    check(
+        assert_type(df.groupby("col1").apply(sample_to_df), pd.DataFrame), pd.DataFrame
+    )