CLN: Remove unused code

pandas/core/apply.py

@@ -12,7 +12,6 @@
     Literal,
     cast,
 )
-import warnings
 
 import numpy as np
 
@@ -30,7 +29,6 @@
 from pandas.compat._optional import import_optional_dependency
 from pandas.errors import SpecificationError
 from pandas.util._decorators import cache_readonly
-from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.cast import is_nested_object
 from pandas.core.dtypes.common import (
@@ -1992,23 +1990,3 @@ def include_axis(op_name: Literal["agg", "apply"], colg: Series | DataFrame) ->
     return isinstance(colg, ABCDataFrame) or (
         isinstance(colg, ABCSeries) and op_name == "agg"
     )
-
-
-def warn_alias_replacement(
-    obj: AggObjType,
-    func: Callable,
-    alias: str,
-) -> None:
-    if alias.startswith("np."):
-        full_alias = alias
-    else:
-        full_alias = f"{type(obj).__name__}.{alias}"
-        alias = f'"{alias}"'
-    warnings.warn(
-        f"The provided callable {func} is currently using "
-        f"{full_alias}. In a future version of pandas, "
-        f"the provided callable will be used directly. To keep current "
-        f"behavior pass the string {alias} instead.",
-        category=FutureWarning,
-        stacklevel=find_stack_level(),
-    )

pandas/core/arrays/sparse/array.py

@@ -673,12 +673,6 @@ def __len__(self) -> int:
     def _null_fill_value(self) -> bool:
         return self._dtype._is_na_fill_value
 
-    def _fill_value_matches(self, fill_value) -> bool:
-        if self._null_fill_value:
-            return isna(fill_value)
-        else:
-            return self.fill_value == fill_value
-
     @property
     def nbytes(self) -> int:
         return self.sp_values.nbytes + self.sp_index.nbytes

pandas/core/groupby/categorical.py

@@ -10,9 +10,7 @@
 )
 
 
-def recode_for_groupby(
-    c: Categorical, sort: bool, observed: bool
-) -> tuple[Categorical, Categorical | None]:
+def recode_for_groupby(c: Categorical, sort: bool, observed: bool) -> Categorical:
     """
     Code the categories to ensure we can groupby for categoricals.
 
@@ -42,8 +40,6 @@ def recode_for_groupby(
         appearance in codes (unless ordered=True, in which case the
         original order is preserved), followed by any unrepresented
         categories in the original order.
-    Categorical or None
-        If we are observed, return the original categorical, otherwise None
     """
     # we only care about observed values
     if observed:
@@ -63,11 +59,11 @@ def recode_for_groupby(
         # return a new categorical that maps our new codes
         # and categories
         dtype = CategoricalDtype(categories, ordered=c.ordered)
-        return Categorical._simple_new(codes, dtype=dtype), c
+        return Categorical._simple_new(codes, dtype=dtype)
 
     # Already sorted according to c.categories; all is fine
     if sort:
-        return c, None
+        return c
 
     # sort=False should order groups in as-encountered order (GH-8868)
 
@@ -84,4 +80,4 @@ def recode_for_groupby(
     else:
         take_codes = unique_notnan_codes
 
-    return Categorical(c, c.unique().categories.take(take_codes)), None
+    return Categorical(c, c.unique().categories.take(take_codes))

pandas/core/groupby/grouper.py

@@ -238,7 +238,6 @@ class Grouper:
 
     sort: bool
     dropna: bool
-    _gpr_index: Index | None
     _grouper: Index | None
 
     _attributes: tuple[str, ...] = ("key", "level", "freq", "sort", "dropna")
@@ -266,8 +265,6 @@ def __init__(
 
         self._grouper_deprecated = None
         self._indexer_deprecated: npt.NDArray[np.intp] | None = None
-        self._obj_deprecated = None
-        self._gpr_index = None
         self.binner = None
         self._grouper = None
         self._indexer: npt.NDArray[np.intp] | None = None
@@ -380,10 +377,6 @@ def _set_grouper(
             ax = ax.take(indexer)
             obj = obj.take(indexer, axis=0)
 
-        # error: Incompatible types in assignment (expression has type
-        # "NDFrameT", variable has type "None")
-        self._obj_deprecated = obj  # type: ignore[assignment]
-        self._gpr_index = ax
         return obj, ax, indexer
 
     @final
@@ -433,7 +426,6 @@ class Grouping:
     """
 
     _codes: npt.NDArray[np.signedinteger] | None = None
-    _all_grouper: Categorical | None
     _orig_cats: Index | None
     _index: Index
 
@@ -452,7 +444,6 @@ def __init__(
         self.level = level
         self._orig_grouper = grouper
         grouping_vector = _convert_grouper(index, grouper)
-        self._all_grouper = None
         self._orig_cats = None
         self._index = index
         self._sort = sort
@@ -536,9 +527,7 @@ def __init__(
         elif isinstance(getattr(grouping_vector, "dtype", None), CategoricalDtype):
             # a passed Categorical
             self._orig_cats = grouping_vector.categories
-            grouping_vector, self._all_grouper = recode_for_groupby(
-                grouping_vector, sort, observed
-            )
+            grouping_vector = recode_for_groupby(grouping_vector, sort, observed)
 
         self.grouping_vector = grouping_vector
 

pandas/core/internals/managers.py

@@ -267,12 +267,6 @@ def __nonzero__(self) -> bool:
     # Python3 compat
     __bool__ = __nonzero__
 
-    def _normalize_axis(self, axis: AxisInt) -> int:
-        # switch axis to follow BlockManager logic
-        if self.ndim == 2:
-            axis = 1 if axis == 0 else 0
-        return axis
-
     def set_axis(self, axis: AxisInt, new_labels: Index) -> None:
         # Caller is responsible for ensuring we have an Index object.
         self._validate_set_axis(axis, new_labels)
@@ -446,14 +440,6 @@ def apply(
         out = type(self).from_blocks(result_blocks, self.axes)
         return out
 
-    def apply_with_block(
-        self,
-        f,
-        align_keys: list[str] | None = None,
-        **kwargs,
-    ) -> Self:
-        raise AbstractMethodError(self)
-
     @final
     def isna(self, func) -> Self:
         return self.apply("apply", func=func)