CLN: Remove unused functions

pandas/compat/numpy/function.py

@@ -258,10 +258,6 @@ def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
     MINMAX_DEFAULTS, fname="max", method="both", max_fname_arg_count=1
 )
 
-RESHAPE_DEFAULTS: dict[str, str] = {"order": "C"}
-validate_reshape = CompatValidator(
-    RESHAPE_DEFAULTS, fname="reshape", method="both", max_fname_arg_count=1
-)
 
 REPEAT_DEFAULTS: dict[str, Any] = {"axis": None}
 validate_repeat = CompatValidator(
@@ -273,12 +269,6 @@ def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
     ROUND_DEFAULTS, fname="round", method="both", max_fname_arg_count=1
 )
 
-SORT_DEFAULTS: dict[str, int | str | None] = {}
-SORT_DEFAULTS["axis"] = -1
-SORT_DEFAULTS["kind"] = "quicksort"
-SORT_DEFAULTS["order"] = None
-validate_sort = CompatValidator(SORT_DEFAULTS, fname="sort", method="kwargs")
-
 STAT_FUNC_DEFAULTS: dict[str, Any | None] = {}
 STAT_FUNC_DEFAULTS["dtype"] = None
 STAT_FUNC_DEFAULTS["out"] = None
@@ -324,20 +314,6 @@ def validate_cum_func_with_skipna(skipna: bool, args, kwargs, name) -> bool:
 validate_take = CompatValidator(TAKE_DEFAULTS, fname="take", method="kwargs")
 
 
-def validate_take_with_convert(convert: ndarray | bool | None, args, kwargs) -> bool:
-    """
-    If this function is called via the 'numpy' library, the third parameter in
-    its signature is 'axis', which takes either an ndarray or 'None', so check
-    if the 'convert' parameter is either an instance of ndarray or is None
-    """
-    if isinstance(convert, ndarray) or convert is None:
-        args = (convert,) + args
-        convert = True
-
-    validate_take(args, kwargs, max_fname_arg_count=3, method="both")
-    return convert
-
-
 TRANSPOSE_DEFAULTS = {"axes": None}
 validate_transpose = CompatValidator(
     TRANSPOSE_DEFAULTS, fname="transpose", method="both", max_fname_arg_count=0
@@ -362,23 +338,6 @@ def validate_groupby_func(name: str, args, kwargs, allowed=None) -> None:
         )
 
 
-RESAMPLER_NUMPY_OPS = ("min", "max", "sum", "prod", "mean", "std", "var")
-
-
-def validate_resampler_func(method: str, args, kwargs) -> None:
-    """
-    'args' and 'kwargs' should be empty because all of their necessary
-    parameters are explicitly listed in the function signature
-    """
-    if len(args) + len(kwargs) > 0:
-        if method in RESAMPLER_NUMPY_OPS:
-            raise UnsupportedFunctionCall(
-                "numpy operations are not valid with resample. "
-                f"Use .resample(...).{method}() instead"
-            )
-        raise TypeError("too many arguments passed in")
-
-
 def validate_minmax_axis(axis: AxisInt | None, ndim: int = 1) -> None:
     """
     Ensure that the axis argument passed to min, max, argmin, or argmax is zero

pandas/core/internals/blocks.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from functools import wraps
 import inspect
 import re
 from typing import (
@@ -31,7 +30,6 @@
     AxisInt,
     DtypeBackend,
     DtypeObj,
-    F,
     FillnaOptions,
     IgnoreRaise,
     InterpolateOptions,
@@ -131,23 +129,6 @@
 _dtype_obj = np.dtype("object")
 
 
-def maybe_split(meth: F) -> F:
-    """
-    If we have a multi-column block, split and operate block-wise.  Otherwise
-    use the original method.
-    """
-
-    @wraps(meth)
-    def newfunc(self, *args, **kwargs) -> list[Block]:
-        if self.ndim == 1 or self.shape[0] == 1:
-            return meth(self, *args, **kwargs)
-        else:
-            # Split and operate column-by-column
-            return self.split_and_operate(meth, *args, **kwargs)
-
-    return cast(F, newfunc)
-
-
 class Block(PandasObject, libinternals.Block):
     """
     Canonical n-dimensional unit of homogeneous dtype contained in a pandas

pandas/core/methods/describe.py

@@ -18,7 +18,6 @@
 
 import numpy as np
 
-from pandas._libs.tslibs import Timestamp
 from pandas._typing import (
     DtypeObj,
     NDFrameT,
@@ -288,54 +287,6 @@ def describe_categorical_1d(
     return Series(result, index=names, name=data.name, dtype=dtype)
 
 
-def describe_timestamp_as_categorical_1d(
-    data: Series,
-    percentiles_ignored: Sequence[float],
-) -> Series:
-    """Describe series containing timestamp data treated as categorical.
-
-    Parameters
-    ----------
-    data : Series
-        Series to be described.
-    percentiles_ignored : list-like of numbers
-        Ignored, but in place to unify interface.
-    """
-    names = ["count", "unique"]
-    objcounts = data.value_counts()
-    count_unique = len(objcounts[objcounts != 0])
-    result: list[float | Timestamp] = [data.count(), count_unique]
-    dtype = None
-    if count_unique > 0:
-        top, freq = objcounts.index[0], objcounts.iloc[0]
-        tz = data.dt.tz
-        asint = data.dropna().values.view("i8")
-        top = Timestamp(top)
-        if top.tzinfo is not None and tz is not None:
-            # Don't tz_localize(None) if key is already tz-aware
-            top = top.tz_convert(tz)
-        else:
-            top = top.tz_localize(tz)
-        names += ["top", "freq", "first", "last"]
-        result += [
-            top,
-            freq,
-            Timestamp(asint.min(), tz=tz),
-            Timestamp(asint.max(), tz=tz),
-        ]
-
-    # If the DataFrame is empty, set 'top' and 'freq' to None
-    # to maintain output shape consistency
-    else:
-        names += ["top", "freq"]
-        result += [np.nan, np.nan]
-        dtype = "object"
-
-    from pandas import Series
-
-    return Series(result, index=names, name=data.name, dtype=dtype)
-
-
 def describe_timestamp_1d(data: Series, percentiles: Sequence[float]) -> Series:
     """Describe series containing datetime64 dtype.
 

pandas/core/sorting.py

@@ -2,11 +2,9 @@
 
 from __future__ import annotations
 
-from collections import defaultdict
 from typing import (
     TYPE_CHECKING,
     Callable,
-    DefaultDict,
     cast,
 )
 
@@ -34,7 +32,6 @@
 if TYPE_CHECKING:
     from collections.abc import (
         Hashable,
-        Iterable,
         Sequence,
     )
 
@@ -592,23 +589,6 @@ def ensure_key_mapped(
     return result
 
 
-def get_flattened_list(
-    comp_ids: npt.NDArray[np.intp],
-    ngroups: int,
-    levels: Iterable[Index],
-    labels: Iterable[np.ndarray],
-) -> list[tuple]:
-    """Map compressed group id -> key tuple."""
-    comp_ids = comp_ids.astype(np.int64, copy=False)
-    arrays: DefaultDict[int, list[int]] = defaultdict(list)
-    for labs, level in zip(labels, levels):
-        table = hashtable.Int64HashTable(ngroups)
-        table.map_keys_to_values(comp_ids, labs.astype(np.int64, copy=False))
-        for i in range(ngroups):
-            arrays[i].append(level[table.get_item(i)])
-    return [tuple(array) for array in arrays.values()]
-
-
 def get_indexer_dict(
     label_list: list[np.ndarray], keys: list[Index]
 ) -> dict[Hashable, npt.NDArray[np.intp]]: