CLN/TYP: assorted cleanup and annotations

pandas/_libs/tslib.pyx

@@ -235,7 +235,10 @@ def array_with_unit_to_datetime(
         if issubclass(values.dtype.type, (np.integer, np.float_)):
             result = values.astype("M8[ns]", copy=False)
         else:
-            result, tz = array_to_datetime(values.astype(object), errors=errors)
+            result, tz = array_to_datetime(
+                values.astype(object, copy=False),
+                errors=errors,
+            )
         return result, tz
 
     m, p = precision_from_unit(unit)

pandas/core/base.py

@@ -196,6 +196,7 @@ def _selected_obj(self):
         else:
             return self.obj[self._selection]
 
+    @final
     @cache_readonly
     def ndim(self) -> int:
         return self._selected_obj.ndim

pandas/core/groupby/generic.py

@@ -1105,8 +1105,8 @@ def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
         else:
             # we get here in a number of test_multilevel tests
             for name in self.indices:
-                data = self.get_group(name, obj=obj)
-                fres = func(data, *args, **kwargs)
+                grp_df = self.get_group(name, obj=obj)
+                fres = func(grp_df, *args, **kwargs)
                 result[name] = fres
 
         result_index = self.grouper.result_index

pandas/core/groupby/groupby.py

@@ -525,7 +525,7 @@ class GroupByPlot(PandasObject):
     Class implementing the .plot attribute for groupby objects.
     """
 
-    def __init__(self, groupby):
+    def __init__(self, groupby: GroupBy):
         self._groupby = groupby
 
     def __call__(self, *args, **kwargs):
@@ -727,7 +727,7 @@ def pipe(
     plot = property(GroupByPlot)
 
     @final
-    def get_group(self, name, obj=None):
+    def get_group(self, name, obj=None) -> FrameOrSeriesUnion:
         """
         Construct DataFrame from group with provided name.
 
@@ -960,10 +960,11 @@ def _set_group_selection(self) -> None:
 
         NOTE: this should be paired with a call to _reset_group_selection
         """
+        # This is a no-op for SeriesGroupBy
         grp = self.grouper
         if not (
             self.as_index
-            and getattr(grp, "groupings", None) is not None
+            and grp.groupings is not None
             and self.obj.ndim > 1
             and self._group_selection is None
         ):

pandas/core/internals/construction.py

@@ -117,7 +117,7 @@ def arrays_to_mgr(
     if verify_integrity:
         # figure out the index, if necessary
         if index is None:
-            index = extract_index(arrays)
+            index = _extract_index(arrays)
         else:
             index = ensure_index(index)
 
@@ -424,7 +424,7 @@ def dict_to_mgr(
         if index is None:
             # GH10856
             # raise ValueError if only scalars in dict
-            index = extract_index(arrays[~missing])
+            index = _extract_index(arrays[~missing])
         else:
             index = ensure_index(index)
 
@@ -603,7 +603,7 @@ def _homogenize(data, index: Index, dtype: DtypeObj | None):
     return homogenized
 
 
-def extract_index(data) -> Index:
+def _extract_index(data) -> Index:
     """
     Try to infer an Index from the passed data, raise ValueError on failure.
     """

pandas/core/tools/datetimes.py

@@ -497,26 +497,20 @@ def _to_datetime_with_format(
                 return _box_as_indexlike(result, utc=utc, name=name)
 
         # fallback
-        if result is None:
-            res = _array_strptime_with_fallback(
-                arg, name, tz, fmt, exact, errors, infer_datetime_format
-            )
-            if res is not None:
-                return res
+        res = _array_strptime_with_fallback(
+            arg, name, tz, fmt, exact, errors, infer_datetime_format
+        )
+        return res
 
-    except ValueError as e:
+    except ValueError as err:
         # Fallback to try to convert datetime objects if timezone-aware
         #  datetime objects are found without passing `utc=True`
         try:
             values, tz = conversion.datetime_to_datetime64(arg)
             dta = DatetimeArray(values, dtype=tz_to_dtype(tz))
             return DatetimeIndex._simple_new(dta, name=name)
         except (ValueError, TypeError):
-            raise e
-
-    # error: Incompatible return value type (got "Optional[ndarray]", expected
-    # "Optional[Index]")
-    return result  # type: ignore[return-value]
+            raise err
 
 
 def _to_datetime_with_unit(arg, unit, name, tz, errors: str) -> Index:

pandas/core/window/rolling.py

@@ -111,6 +111,7 @@ class BaseWindow(SelectionMixin):
 
     _attributes: list[str] = []
     exclusions: frozenset[Hashable] = frozenset()
+    _on: Index
 
     def __init__(
         self,
@@ -169,7 +170,7 @@ def win_type(self):
         return self._win_type
 
     @property
-    def is_datetimelike(self):
+    def is_datetimelike(self) -> bool:
         warnings.warn(
             "is_datetimelike is deprecated and will be removed in a future version.",
             FutureWarning,
@@ -329,7 +330,7 @@ def _prep_values(self, values: ArrayLike) -> np.ndarray:
         # expected "ndarray")
         return values  # type: ignore[return-value]
 
-    def _insert_on_column(self, result: DataFrame, obj: DataFrame):
+    def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None:
         # if we have an 'on' column we want to put it back into
         # the results in the same location
         from pandas import Series

pandas/tests/tools/test_to_datetime.py

@@ -1079,7 +1079,7 @@ def test_iso8601_strings_mixed_offsets_with_naive(self):
 
     def test_mixed_offsets_with_native_datetime_raises(self):
         # GH 25978
-        s = Series(
+        ser = Series(
             [
                 "nan",
                 Timestamp("1990-01-01"),
@@ -1089,7 +1089,7 @@ def test_mixed_offsets_with_native_datetime_raises(self):
             ]
         )
         with pytest.raises(ValueError, match="Tz-aware datetime.datetime"):
-            to_datetime(s)
+            to_datetime(ser)
 
     def test_non_iso_strings_with_tz_offset(self):
         result = to_datetime(["March 1, 2018 12:00:00+0400"] * 2)