CLN: Remove unused code (#57851)

* CLN: Remove unused code * CLN: Fix docstring

Author: tqa236

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)