remove changes that created aliases with leading underscore

Author: Dr-Irv

pandas/core/apply.py

@@ -72,7 +72,7 @@
     from pandas.core.resample import Resampler
     from pandas.core.window.rolling import BaseWindow
 
-_ResType: TypeAlias = dict[int, Any]
+ResType: TypeAlias = dict[int, Any]
 
 
 class BaseExecutionEngine(abc.ABC):
@@ -935,7 +935,7 @@ def validate_values_for_numba(self) -> None:
 
     @abc.abstractmethod
     def wrap_results_for_axis(
-        self, results: _ResType, res_index: Index
+        self, results: ResType, res_index: Index
     ) -> DataFrame | Series:
         pass
 
@@ -1164,7 +1164,7 @@ def apply_standard(self):
         # wrap results
         return self.wrap_results(results, res_index)
 
-    def apply_series_generator(self) -> tuple[_ResType, Index]:
+    def apply_series_generator(self) -> tuple[ResType, Index]:
         assert callable(self.func)
 
         series_gen = self.series_generator
@@ -1194,7 +1194,7 @@ def apply_series_numba(self):
         results = self.apply_with_numba()
         return results, self.result_index
 
-    def wrap_results(self, results: _ResType, res_index: Index) -> DataFrame | Series:
+    def wrap_results(self, results: ResType, res_index: Index) -> DataFrame | Series:
         from pandas import Series
 
         # see if we can infer the results
@@ -1290,7 +1290,7 @@ def result_columns(self) -> Index:
         return self.index
 
     def wrap_results_for_axis(
-        self, results: _ResType, res_index: Index
+        self, results: ResType, res_index: Index
     ) -> DataFrame | Series:
         """return the results for the rows"""
 
@@ -1434,7 +1434,7 @@ def result_columns(self) -> Index:
         return self.columns
 
     def wrap_results_for_axis(
-        self, results: _ResType, res_index: Index
+        self, results: ResType, res_index: Index
     ) -> DataFrame | Series:
         """return the results for the columns"""
         result: DataFrame | Series
@@ -1454,7 +1454,7 @@ def wrap_results_for_axis(
 
         return result
 
-    def infer_to_same_shape(self, results: _ResType, res_index: Index) -> DataFrame:
+    def infer_to_same_shape(self, results: ResType, res_index: Index) -> DataFrame:
         """infer the results to the same shape as the input object"""
         result = self.obj._constructor(data=results)
         result = result.T

pandas/core/arrays/datetimelike.py

@@ -162,8 +162,7 @@
         TimedeltaArray,
     )
 
-# underscore at end because of rule PYI043 that private types should not end with 'T'
-_DTScalarOrNaT_: TypeAlias = DatetimeLikeScalar | NaTType
+DTScalarOrNaT: TypeAlias = DatetimeLikeScalar | NaTType
 
 
 def _make_unpacked_invalid_op(op_name: str):
@@ -238,7 +237,7 @@ def _scalar_type(self) -> type[DatetimeLikeScalar]:
         """
         raise AbstractMethodError(self)
 
-    def _scalar_from_string(self, value: str) -> _DTScalarOrNaT_:
+    def _scalar_from_string(self, value: str) -> DTScalarOrNaT:
         """
         Construct a scalar type from a string.
 
@@ -259,7 +258,7 @@ def _scalar_from_string(self, value: str) -> _DTScalarOrNaT_:
         raise AbstractMethodError(self)
 
     def _unbox_scalar(
-        self, value: _DTScalarOrNaT_
+        self, value: DTScalarOrNaT
     ) -> np.int64 | np.datetime64 | np.timedelta64:
         """
         Unbox the integer value of a scalar `value`.
@@ -281,7 +280,7 @@ def _unbox_scalar(
         """
         raise AbstractMethodError(self)
 
-    def _check_compatible_with(self, other: _DTScalarOrNaT_) -> None:
+    def _check_compatible_with(self, other: DTScalarOrNaT) -> None:
         """
         Verify that `self` and `other` are compatible.
 
@@ -372,23 +371,23 @@ def __array__(
         return self._ndarray
 
     @overload
-    def __getitem__(self, key: ScalarIndexer) -> _DTScalarOrNaT_: ...
+    def __getitem__(self, key: ScalarIndexer) -> DTScalarOrNaT: ...
 
     @overload
     def __getitem__(
         self,
         key: SequenceIndexer | PositionalIndexerTuple,
     ) -> Self: ...
 
-    def __getitem__(self, key: PositionalIndexer2D) -> Self | _DTScalarOrNaT_:
+    def __getitem__(self, key: PositionalIndexer2D) -> Self | DTScalarOrNaT:
         """
         This getitem defers to the underlying array, which by-definition can
         only handle list-likes, slices, and integer scalars
         """
         # Use cast as we know we will get back a DatetimeLikeArray or DTScalar,
         # but skip evaluating the Union at runtime for performance
         # (see https://github.com/pandas-dev/pandas/pull/44624)
-        result = cast(Union[Self, _DTScalarOrNaT_], super().__getitem__(key))
+        result = cast(Union[Self, DTScalarOrNaT], super().__getitem__(key))
         if lib.is_scalar(result):
             return result
         else:

pandas/core/arrays/interval.py

@@ -109,8 +109,8 @@
     )
 
 
-_IntervalSide: TypeAlias = TimeArrayLike | np.ndarray
-_IntervalOrNA: TypeAlias = Interval | float
+IntervalSide: TypeAlias = TimeArrayLike | np.ndarray
+IntervalOrNA: TypeAlias = Interval | float
 
 _interval_shared_docs: dict[str, str] = {}
 
@@ -216,8 +216,8 @@ def ndim(self) -> Literal[1]:
         return 1
 
     # To make mypy recognize the fields
-    _left: _IntervalSide
-    _right: _IntervalSide
+    _left: IntervalSide
+    _right: IntervalSide
     _dtype: IntervalDtype
 
     # ---------------------------------------------------------------------
@@ -234,8 +234,8 @@ def __new__(
         data = extract_array(data, extract_numpy=True)
 
         if isinstance(data, cls):
-            left: _IntervalSide = data._left
-            right: _IntervalSide = data._right
+            left: IntervalSide = data._left
+            right: IntervalSide = data._right
             closed = closed or data.closed
             dtype = IntervalDtype(left.dtype, closed=closed)
         else:
@@ -277,8 +277,8 @@ def __new__(
     @classmethod
     def _simple_new(
         cls,
-        left: _IntervalSide,
-        right: _IntervalSide,
+        left: IntervalSide,
+        right: IntervalSide,
         dtype: IntervalDtype,
     ) -> Self:
         result = IntervalMixin.__new__(cls)
@@ -296,7 +296,7 @@ def _ensure_simple_new_inputs(
         closed: IntervalClosedType | None = None,
         copy: bool = False,
         dtype: Dtype | None = None,
-    ) -> tuple[_IntervalSide, _IntervalSide, IntervalDtype]:
+    ) -> tuple[IntervalSide, IntervalSide, IntervalDtype]:
         """Ensure correctness of input parameters for cls._simple_new."""
         from pandas.core.indexes.base import ensure_index
 
@@ -704,12 +704,12 @@ def __len__(self) -> int:
         return len(self._left)
 
     @overload
-    def __getitem__(self, key: ScalarIndexer) -> _IntervalOrNA: ...
+    def __getitem__(self, key: ScalarIndexer) -> IntervalOrNA: ...
 
     @overload
     def __getitem__(self, key: SequenceIndexer) -> Self: ...
 
-    def __getitem__(self, key: PositionalIndexer) -> Self | _IntervalOrNA:
+    def __getitem__(self, key: PositionalIndexer) -> Self | IntervalOrNA:
         key = check_array_indexer(self, key)
         left = self._left[key]
         right = self._right[key]
@@ -858,7 +858,7 @@ def argsort(
             ascending=ascending, kind=kind, na_position=na_position, **kwargs
         )
 
-    def min(self, *, axis: AxisInt | None = None, skipna: bool = True) -> _IntervalOrNA:
+    def min(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
         nv.validate_minmax_axis(axis, self.ndim)
 
         if not len(self):
@@ -875,7 +875,7 @@ def min(self, *, axis: AxisInt | None = None, skipna: bool = True) -> _IntervalO
         indexer = obj.argsort()[0]
         return obj[indexer]
 
-    def max(self, *, axis: AxisInt | None = None, skipna: bool = True) -> _IntervalOrNA:
+    def max(self, *, axis: AxisInt | None = None, skipna: bool = True) -> IntervalOrNA:
         nv.validate_minmax_axis(axis, self.ndim)
 
         if not len(self):
@@ -1016,10 +1016,8 @@ def _concat_same_type(cls, to_concat: Sequence[IntervalArray]) -> Self:
             raise ValueError("Intervals must all be closed on the same side.")
         closed = closed_set.pop()
 
-        left: _IntervalSide = np.concatenate([interval.left for interval in to_concat])
-        right: _IntervalSide = np.concatenate(
-            [interval.right for interval in to_concat]
-        )
+        left: IntervalSide = np.concatenate([interval.left for interval in to_concat])
+        right: IntervalSide = np.concatenate([interval.right for interval in to_concat])
 
         left, right, dtype = cls._ensure_simple_new_inputs(left, right, closed=closed)
 
@@ -1954,7 +1952,7 @@ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
         return isin(self.astype(object), values.astype(object))
 
     @property
-    def _combined(self) -> _IntervalSide:
+    def _combined(self) -> IntervalSide:
         # error: Item "ExtensionArray" of "ExtensionArray | ndarray[Any, Any]"
         # has no attribute "reshape"  [union-attr]
         left = self.left._values.reshape(-1, 1)  # type: ignore[union-attr]

pandas/core/groupby/generic.py

@@ -102,7 +102,7 @@
     from pandas.core.generic import NDFrame
 
 # TODO(typing) the return value on this callable should be any *scalar*.
-_AggScalar: TypeAlias = str | Callable[..., Any]
+AggScalar: TypeAlias = str | Callable[..., Any]
 # TODO: validate types on ScalarResult and move to _typing
 # Blocked from using by https://github.com/python/mypy/issues/1484
 # See note at _mangle_lambda_list
@@ -141,7 +141,7 @@ class NamedAgg(NamedTuple):
     """
 
     column: Hashable
-    aggfunc: _AggScalar
+    aggfunc: AggScalar
 
 
 @set_module("pandas.api.typing")

pandas/core/tools/datetimes.py

@@ -94,33 +94,33 @@
 # ---------------------------------------------------------------------
 # types used in annotations
 
-_ArrayConvertible: TypeAlias = list | tuple | AnyArrayLike
-_Scalar: TypeAlias = float | str
-_DatetimeScalar: TypeAlias = _Scalar | date | np.datetime64
+ArrayConvertible: TypeAlias = list | tuple | AnyArrayLike
+Scalar: TypeAlias = float | str
+DatetimeScalar: TypeAlias = Scalar | date | np.datetime64
 
-_DatetimeScalarOrArrayConvertible: TypeAlias = _DatetimeScalar | _ArrayConvertible
-_DatetimeDictArg: TypeAlias = list[_Scalar] | tuple[_Scalar, ...] | AnyArrayLike
+DatetimeScalarOrArrayConvertible: TypeAlias = DatetimeScalar | ArrayConvertible
+DatetimeDictArg: TypeAlias = list[Scalar] | tuple[Scalar, ...] | AnyArrayLike
 
 
-class _YearMonthDayDict(TypedDict, total=True):
-    year: _DatetimeDictArg
-    month: _DatetimeDictArg
-    day: _DatetimeDictArg
+class YearMonthDayDict(TypedDict, total=True):
+    year: DatetimeDictArg
+    month: DatetimeDictArg
+    day: DatetimeDictArg
 
 
-class _FulldatetimeDict(_YearMonthDayDict, total=False):
-    hour: _DatetimeDictArg
-    hours: _DatetimeDictArg
-    minute: _DatetimeDictArg
-    minutes: _DatetimeDictArg
-    second: _DatetimeDictArg
-    seconds: _DatetimeDictArg
-    ms: _DatetimeDictArg
-    us: _DatetimeDictArg
-    ns: _DatetimeDictArg
+class FulldatetimeDict(YearMonthDayDict, total=False):
+    hour: DatetimeDictArg
+    hours: DatetimeDictArg
+    minute: DatetimeDictArg
+    minutes: DatetimeDictArg
+    second: DatetimeDictArg
+    seconds: DatetimeDictArg
+    ms: DatetimeDictArg
+    us: DatetimeDictArg
+    ns: DatetimeDictArg
 
 
-_DictConvertible = Union[_FulldatetimeDict, "DataFrame"]
+DictConvertible = Union[FulldatetimeDict, "DataFrame"]
 start_caching_at = 50
 
 
@@ -151,7 +151,7 @@ def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str
 
 
 def should_cache(
-    arg: _ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
+    arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
 ) -> bool:
     """
     Decides whether to do caching.
@@ -211,7 +211,7 @@ def should_cache(
 
 
 def _maybe_cache(
-    arg: _ArrayConvertible,
+    arg: ArrayConvertible,
     format: str | None,
     cache: bool,
     convert_listlike: Callable,
@@ -290,7 +290,7 @@ def _box_as_indexlike(
 
 
 def _convert_and_box_cache(
-    arg: _DatetimeScalarOrArrayConvertible,
+    arg: DatetimeScalarOrArrayConvertible,
     cache_array: Series,
     name: Hashable | None = None,
 ) -> Index:
@@ -622,7 +622,7 @@ def _adjust_to_origin(arg, origin, unit):
 
 @overload
 def to_datetime(
-    arg: _DatetimeScalar,
+    arg: DatetimeScalar,
     errors: DateTimeErrorChoices = ...,
     dayfirst: bool = ...,
     yearfirst: bool = ...,
@@ -637,7 +637,7 @@ def to_datetime(
 
 @overload
 def to_datetime(
-    arg: Series | _DictConvertible,
+    arg: Series | DictConvertible,
     errors: DateTimeErrorChoices = ...,
     dayfirst: bool = ...,
     yearfirst: bool = ...,
@@ -666,7 +666,7 @@ def to_datetime(
 
 
 def to_datetime(
-    arg: _DatetimeScalarOrArrayConvertible | _DictConvertible,
+    arg: DatetimeScalarOrArrayConvertible | DictConvertible,
     errors: DateTimeErrorChoices = "raise",
     dayfirst: bool = False,
     yearfirst: bool = False,
@@ -676,7 +676,7 @@ def to_datetime(
     unit: str | None = None,
     origin: str = "unix",
     cache: bool = True,
-) -> DatetimeIndex | Series | _DatetimeScalar | NaTType | None:
+) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
     """
     Convert argument to datetime.
 

pandas/io/formats/printing.py

@@ -27,7 +27,7 @@
 
 if TYPE_CHECKING:
     from pandas._typing import ListLike
-_EscapeChars: TypeAlias = Mapping[str, str] | Iterable[str]
+EscapeChars: TypeAlias = Mapping[str, str] | Iterable[str]
 _KT = TypeVar("_KT")
 _VT = TypeVar("_VT")
 
@@ -174,7 +174,7 @@ def _pprint_dict(
 def pprint_thing(
     thing: object,
     _nest_lvl: int = 0,
-    escape_chars: _EscapeChars | None = None,
+    escape_chars: EscapeChars | None = None,
     default_escapes: bool = False,
     quote_strings: bool = False,
     max_seq_items: int | None = None,
@@ -203,7 +203,7 @@ def pprint_thing(
     """
 
     def as_escaped_string(
-        thing: Any, escape_chars: _EscapeChars | None = escape_chars
+        thing: Any, escape_chars: EscapeChars | None = escape_chars
     ) -> str:
         translate = {"\t": r"\t", "\n": r"\n", "\r": r"\r", "'": r"\'"}
         if isinstance(escape_chars, Mapping):