Remove unused/single use methods (#14739)

* Didn't see use of `get_numeric_type_info`
* `Column._minmax` was only used once. Replaced with `Column._reduce`
* `CategoryColumn.unary_operator` raises like the base class

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Ashwin Srinath (https://github.com/shwina)

URL: #14739

python/cudf/cudf/core/column/categorical.py

@@ -823,12 +823,6 @@ def ordered(self) -> bool:
     def ordered(self, value: bool):
         self.dtype.ordered = value
 
-    def unary_operator(self, unaryop: str):
-        raise TypeError(
-            f"Series of dtype `category` cannot perform the operation: "
-            f"{unaryop}"
-        )
-
     def __setitem__(self, key, value):
         if cudf.api.types.is_scalar(
             value

python/cudf/cudf/core/column/column.py

@@ -1241,12 +1241,6 @@ def normalize_binop_value(
     ) -> Union[ColumnBase, ScalarLike]:
         raise NotImplementedError
 
-    def _minmax(self, skipna: Optional[bool] = None):
-        result_col = self._process_for_reduction(skipna=skipna)
-        if isinstance(result_col, ColumnBase):
-            return libcudf.reduce.minmax(result_col)
-        return result_col
-
     def _reduce(
         self,
         op: str,
@@ -1273,13 +1267,13 @@ def _reduce(
     def _process_for_reduction(
         self, skipna: Optional[bool] = None, min_count: int = 0
     ) -> Union[ColumnBase, ScalarLike]:
-        skipna = True if skipna is None else skipna
+        if skipna is None:
+            skipna = True
 
-        if skipna:
-            if self.has_nulls():
+        if self.has_nulls():
+            if skipna:
                 result_col = self.dropna()
-        else:
-            if self.has_nulls():
+            else:
                 return cudf.utils.dtypes._get_nan_for_dtype(self.dtype)
 
         result_col = self

python/cudf/cudf/core/resample.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2023, NVIDIA CORPORATION &
+# SPDX-FileCopyrightText: Copyright (c) 2021-2024, NVIDIA CORPORATION &
 # AFFILIATES. All rights reserved.  SPDX-License-Identifier:
 # Apache-2.0
 #
@@ -217,10 +217,11 @@ def _handle_frequency_grouper(self, by):
 
         # get the start and end values that will be used to generate
         # the bin labels
-        min_date, max_date = key_column._minmax()
+        min_date = key_column._reduce("min")
+        max_date = key_column._reduce("max")
         start, end = _get_timestamp_range_edges(
-            pd.Timestamp(min_date.value),
-            pd.Timestamp(max_date.value),
+            pd.Timestamp(min_date),
+            pd.Timestamp(max_date),
             offset,
             closed=closed,
         )

python/cudf/cudf/utils/dtypes.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 import datetime
-from collections import namedtuple
 from decimal import Decimal
 
 import cupy as cp
@@ -139,17 +138,6 @@ def np_to_pa_dtype(dtype):
     return _np_pa_dtypes[cudf.dtype(dtype).type]
 
 
-def get_numeric_type_info(dtype):
-    _TypeMinMax = namedtuple("_TypeMinMax", "min,max")
-    if dtype.kind in {"i", "u"}:
-        info = np.iinfo(dtype)
-        return _TypeMinMax(info.min, info.max)
-    elif dtype.kind == "f":
-        return _TypeMinMax(dtype.type("-inf"), dtype.type("+inf"))
-    else:
-        raise TypeError(dtype)
-
-
 def numeric_normalize_types(*args):
     """Cast all args to a common type using numpy promotion logic"""
     dtype = np.result_type(*[a.dtype for a in args])