diff --git a/python/cudf/cudf/_lib/groupby.pyx b/python/cudf/cudf/_lib/groupby.pyx
index b3778e45cde..f332fead8d1 100644
--- a/python/cudf/cudf/_lib/groupby.pyx
+++ b/python/cudf/cudf/_lib/groupby.pyx
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 from pandas.core.groupby.groupby import DataError
 
@@ -104,7 +104,7 @@ cdef class GroupBy:
     cdef unique_ptr[libcudf_groupby.groupby] c_obj
     cdef dict __dict__
 
-    def __cinit__(self, list keys, bool dropna=True, *args, **kwargs):
+    def __cinit__(self, list keys, bool dropna=True):
         cdef libcudf_types.null_policy c_null_handling
         cdef table_view keys_view
 
diff --git a/python/cudf/cudf/_lib/string_casting.pyx b/python/cudf/cudf/_lib/string_casting.pyx
index 4b44ac83a70..3826e71f850 100644
--- a/python/cudf/cudf/_lib/string_casting.pyx
+++ b/python/cudf/cudf/_lib/string_casting.pyx
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
 
 from cudf._lib.column cimport Column
 
@@ -95,7 +95,7 @@ def dtos(Column input_col):
     return floating_to_string(input_col)
 
 
-def stod(Column input_col, **kwargs):
+def stod(Column input_col):
     """
     Converting/Casting input column of type string to double
 
@@ -127,7 +127,7 @@ def ftos(Column input_col):
     return floating_to_string(input_col)
 
 
-def stof(Column input_col, **kwargs):
+def stof(Column input_col):
     """
     Converting/Casting input column of type string to float
 
@@ -188,7 +188,7 @@ def i8tos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stoi8(Column input_col, **kwargs):
+def stoi8(Column input_col):
     """
     Converting/Casting input column of type string to int8
 
@@ -284,7 +284,7 @@ def ltos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stol(Column input_col, **kwargs):
+def stol(Column input_col):
     """
     Converting/Casting input column of type string to int64
 
@@ -316,7 +316,7 @@ def ui8tos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stoui8(Column input_col, **kwargs):
+def stoui8(Column input_col):
     """
     Converting/Casting input column of type string to uint8
 
@@ -348,7 +348,7 @@ def ui16tos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stoui16(Column input_col, **kwargs):
+def stoui16(Column input_col):
     """
     Converting/Casting input column of type string to uint16
 
@@ -380,7 +380,7 @@ def uitos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stoui(Column input_col, **kwargs):
+def stoui(Column input_col):
     """
     Converting/Casting input column of type string to uint32
 
@@ -412,7 +412,7 @@ def ultos(Column input_col):
     return integer_to_string(input_col)
 
 
-def stoul(Column input_col, **kwargs):
+def stoul(Column input_col):
     """
     Converting/Casting input column of type string to uint64
 
@@ -456,7 +456,7 @@ def _to_booleans(Column input_col, object string_true="True"):
     return Column.from_unique_ptr(move(c_result))
 
 
-def to_booleans(Column input_col, **kwargs):
+def to_booleans(Column input_col):
 
     return _to_booleans(input_col)
 
@@ -631,9 +631,7 @@ def timedelta2int(Column input_col, dtype, format):
     return Column.from_unique_ptr(move(c_result))
 
 
-def int2timedelta(
-        Column input_col,
-        **kwargs):
+def int2timedelta(Column input_col, str format):
     """
     Converting/Casting input Timedelta column to string
     column with specified format
@@ -649,8 +647,7 @@ def int2timedelta(
     """
 
     cdef column_view input_column_view = input_col.view()
-    cdef string c_duration_format = kwargs.get(
-        'format', "%D days %H:%M:%S").encode('UTF-8')
+    cdef string c_duration_format = format.encode('UTF-8')
     cdef unique_ptr[column] c_result
     with nogil:
         c_result = move(
@@ -661,7 +658,7 @@ def int2timedelta(
     return Column.from_unique_ptr(move(c_result))
 
 
-def int2ip(Column input_col, **kwargs):
+def int2ip(Column input_col):
     """
     Converting/Casting integer column to string column in ipv4 format
 
@@ -684,7 +681,7 @@ def int2ip(Column input_col, **kwargs):
     return Column.from_unique_ptr(move(c_result))
 
 
-def ip2int(Column input_col, **kwargs):
+def ip2int(Column input_col):
     """
     Converting string ipv4 column to integer column
 
@@ -732,7 +729,6 @@ def htoi(Column input_col, **kwargs):
     Parameters
     ----------
     input_col : input column of type string
-    out_type : The type of integer column expected
 
     Returns
     -------
@@ -742,9 +738,7 @@ def htoi(Column input_col, **kwargs):
     cdef column_view input_column_view = input_col.view()
     cdef type_id tid = <type_id> (
         <underlying_type_t_type_id> (
-            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[
-                kwargs.get('dtype', cudf.dtype("int64"))
-            ]
+            SUPPORTED_NUMPY_TO_LIBCUDF_TYPES[cudf.dtype("int64")]
         )
     )
     cdef data_type c_out_type = data_type(tid)
diff --git a/python/cudf/cudf/core/column/categorical.py b/python/cudf/cudf/core/column/categorical.py
index 59fd4631067..71143fa7a95 100644
--- a/python/cudf/cudf/core/column/categorical.py
+++ b/python/cudf/cudf/core/column/categorical.py
@@ -1310,22 +1310,28 @@ def as_categorical_column(self, dtype: Dtype) -> CategoricalColumn:
             new_categories=dtype.categories, ordered=bool(dtype.ordered)
         )
 
-    def as_numerical_column(self, dtype: Dtype, **kwargs) -> NumericalColumn:
+    def as_numerical_column(self, dtype: Dtype) -> NumericalColumn:
         return self._get_decategorized_column().as_numerical_column(dtype)
 
-    def as_string_column(self, dtype, format=None, **kwargs) -> StringColumn:
+    def as_string_column(
+        self, dtype, format: str | None = None
+    ) -> StringColumn:
         return self._get_decategorized_column().as_string_column(
             dtype, format=format
         )
 
-    def as_datetime_column(self, dtype, **kwargs) -> DatetimeColumn:
+    def as_datetime_column(
+        self, dtype, format: str | None = None
+    ) -> DatetimeColumn:
         return self._get_decategorized_column().as_datetime_column(
-            dtype, **kwargs
+            dtype, format
         )
 
-    def as_timedelta_column(self, dtype, **kwargs) -> TimeDeltaColumn:
+    def as_timedelta_column(
+        self, dtype, format: str | None = None
+    ) -> TimeDeltaColumn:
         return self._get_decategorized_column().as_timedelta_column(
-            dtype, **kwargs
+            dtype, format
         )
 
     def _get_decategorized_column(self) -> ColumnBase:
diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 5b638b1f4ad..4cb84aec13d 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -978,11 +978,17 @@ def distinct_count(self, dropna: bool = True) -> int:
     def can_cast_safely(self, to_dtype: Dtype) -> bool:
         raise NotImplementedError()
 
-    def astype(self, dtype: Dtype, **kwargs) -> ColumnBase:
+    def astype(
+        self, dtype: Dtype, copy: bool = False, format: str | None = None
+    ) -> ColumnBase:
+        if copy:
+            col = self.copy()
+        else:
+            col = self
         if self.dtype == dtype:
-            return self
+            return col
         if is_categorical_dtype(dtype):
-            return self.as_categorical_column(dtype)
+            return col.as_categorical_column(dtype)
 
         if (
             isinstance(dtype, str)
@@ -999,9 +1005,9 @@ def astype(self, dtype: Dtype, **kwargs) -> ColumnBase:
         else:
             dtype = pandas_dtypes_to_np_dtypes.get(dtype, dtype)
         if _is_non_decimal_numeric_dtype(dtype):
-            return self.as_numerical_column(dtype, **kwargs)
+            return col.as_numerical_column(dtype)
         elif is_categorical_dtype(dtype):
-            return self.as_categorical_column(dtype)
+            return col.as_categorical_column(dtype)
         elif cudf.dtype(dtype).type in {
             np.str_,
             np.object_,
@@ -1014,23 +1020,23 @@ def astype(self, dtype: Dtype, **kwargs) -> ColumnBase:
                     f"Casting to {dtype} is not supported, use "
                     "`.astype('str')` instead."
                 )
-            return self.as_string_column(dtype, **kwargs)
+            return col.as_string_column(dtype, format=format)
         elif isinstance(dtype, (ListDtype, StructDtype)):
-            if not self.dtype == dtype:
+            if not col.dtype == dtype:
                 raise NotImplementedError(
                     f"Casting {self.dtype} columns not currently supported"
                 )
-            return self
+            return col
         elif isinstance(dtype, IntervalDtype):
-            return self.as_interval_column(dtype, **kwargs)
+            return col.as_interval_column(dtype)
         elif isinstance(dtype, cudf.core.dtypes.DecimalDtype):
-            return self.as_decimal_column(dtype, **kwargs)
+            return col.as_decimal_column(dtype)
         elif np.issubdtype(cast(Any, dtype), np.datetime64):
-            return self.as_datetime_column(dtype, **kwargs)
+            return col.as_datetime_column(dtype, format=format)
         elif np.issubdtype(cast(Any, dtype), np.timedelta64):
-            return self.as_timedelta_column(dtype, **kwargs)
+            return col.as_timedelta_column(dtype, format=format)
         else:
-            return self.as_numerical_column(dtype, **kwargs)
+            return col.as_numerical_column(dtype)
 
     def as_categorical_column(self, dtype) -> ColumnBase:
         if isinstance(dtype, (cudf.CategoricalDtype, pd.CategoricalDtype)):
@@ -1076,50 +1082,35 @@ def as_categorical_column(self, dtype) -> ColumnBase:
         )
 
     def as_numerical_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.NumericalColumn":
         raise NotImplementedError
 
     def as_datetime_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.DatetimeColumn":
         raise NotImplementedError
 
     def as_interval_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.IntervalColumn":
         raise NotImplementedError
 
     def as_timedelta_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.TimeDeltaColumn":
         raise NotImplementedError
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         raise NotImplementedError
 
     def as_decimal_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> Union["cudf.core.column.decimal.DecimalBaseColumn"]:
         raise NotImplementedError
 
-    def as_decimal128_column(
-        self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal128Column":
-        raise NotImplementedError
-
-    def as_decimal64_column(
-        self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal64Column":
-        raise NotImplementedError
-
-    def as_decimal32_column(
-        self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal32Column":
-        raise NotImplementedError
-
     def apply_boolean_mask(self, mask) -> ColumnBase:
         mask = as_column(mask)
         if not is_bool_dtype(mask.dtype):
diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py
index 2b44b46bb9e..2ab2dd46c53 100644
--- a/python/cudf/cudf/core/column/datetime.py
+++ b/python/cudf/cudf/core/column/datetime.py
@@ -422,21 +422,23 @@ def __cuda_array_interface__(self) -> Mapping[str, Any]:
             )
         return output
 
-    def as_datetime_column(self, dtype: Dtype, **kwargs) -> DatetimeColumn:
+    def as_datetime_column(
+        self, dtype: Dtype, format: str | None = None
+    ) -> DatetimeColumn:
         dtype = cudf.dtype(dtype)
         if dtype == self.dtype:
             return self
         return libcudf.unary.cast(self, dtype=dtype)
 
     def as_timedelta_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.TimeDeltaColumn":
         raise TypeError(
             f"cannot astype a datetimelike from {self.dtype} to {dtype}"
         )
 
     def as_numerical_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.NumericalColumn":
         col = column.build_column(
             data=self.base_data,
@@ -448,7 +450,7 @@ def as_numerical_column(
         return cast("cudf.core.column.NumericalColumn", col.astype(dtype))
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         if format is None:
             format = _dtype_to_format_conversion.get(
@@ -725,9 +727,9 @@ def _local_time(self):
         return utc_to_local(self, str(self.dtype.tz))
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
-        return self._local_time.as_string_column(dtype, format, **kwargs)
+        return self._local_time.as_string_column(dtype, format)
 
     def get_dt_field(self, field: str) -> ColumnBase:
         return libcudf.datetime.extract_datetime_component(
diff --git a/python/cudf/cudf/core/column/decimal.py b/python/cudf/cudf/core/column/decimal.py
index 299875f0091..0e90b522f2c 100644
--- a/python/cudf/cudf/core/column/decimal.py
+++ b/python/cudf/cudf/core/column/decimal.py
@@ -1,5 +1,7 @@
 # Copyright (c) 2021-2024, NVIDIA CORPORATION.
 
+from __future__ import annotations
+
 import warnings
 from decimal import Decimal
 from typing import Any, Optional, Sequence, Union, cast
@@ -37,7 +39,8 @@ class DecimalBaseColumn(NumericalBaseColumn):
     _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
 
     def as_decimal_column(
-        self, dtype: Dtype, **kwargs
+        self,
+        dtype: Dtype,
     ) -> Union["DecimalBaseColumn"]:
         if (
             isinstance(dtype, cudf.core.dtypes.DecimalDtype)
@@ -53,7 +56,7 @@ def as_decimal_column(
         return libcudf.unary.cast(self, dtype)
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         if len(self) > 0:
             return cpp_from_decimal(self)
@@ -201,7 +204,7 @@ def _decimal_quantile(
         return result._with_type_metadata(self.dtype)
 
     def as_numerical_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.NumericalColumn":
         return libcudf.unary.cast(self, dtype)
 
diff --git a/python/cudf/cudf/core/column/interval.py b/python/cudf/cudf/core/column/interval.py
index eed7bba3628..81059717b20 100644
--- a/python/cudf/cudf/core/column/interval.py
+++ b/python/cudf/cudf/core/column/interval.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2018-2023, NVIDIA CORPORATION.
+# Copyright (c) 2018-2024, NVIDIA CORPORATION.
 from typing import Optional
 
 import pandas as pd
@@ -99,7 +99,7 @@ def copy(self, deep=True):
             closed=closed,
         )
 
-    def as_interval_column(self, dtype, **kwargs):
+    def as_interval_column(self, dtype):
         if isinstance(dtype, IntervalDtype):
             if isinstance(self.dtype, CategoricalDtype):
                 new_struct = self._get_decategorized_column()
diff --git a/python/cudf/cudf/core/column/lists.py b/python/cudf/cudf/core/column/lists.py
index a5653e66513..0cccec6f28a 100644
--- a/python/cudf/cudf/core/column/lists.py
+++ b/python/cudf/cudf/core/column/lists.py
@@ -1,4 +1,6 @@
-# Copyright (c) 2020-2023, NVIDIA CORPORATION.
+# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+
+from __future__ import annotations
 
 from functools import cached_property
 from typing import List, Optional, Sequence, Tuple, Union
@@ -243,7 +245,7 @@ def from_sequences(
         return res
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         """
         Create a strings column from a list column
diff --git a/python/cudf/cudf/core/column/numerical.py b/python/cudf/cudf/core/column/numerical.py
index 148fa252fad..5461d1b13b5 100644
--- a/python/cudf/cudf/core/column/numerical.py
+++ b/python/cudf/cudf/core/column/numerical.py
@@ -340,7 +340,7 @@ def int2ip(self) -> "cudf.core.column.StringColumn":
         return libcudf.string_casting.int2ip(self)
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         if len(self) > 0:
             return string._numeric_to_str_typecast_functions[
@@ -353,7 +353,7 @@ def as_string_column(
             )
 
     def as_datetime_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.DatetimeColumn":
         return cast(
             "cudf.core.column.DatetimeColumn",
@@ -367,7 +367,7 @@ def as_datetime_column(
         )
 
     def as_timedelta_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.TimeDeltaColumn":
         return cast(
             "cudf.core.column.TimeDeltaColumn",
@@ -381,11 +381,11 @@ def as_timedelta_column(
         )
 
     def as_decimal_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.DecimalBaseColumn":
         return libcudf.unary.cast(self, dtype)
 
-    def as_numerical_column(self, dtype: Dtype, **kwargs) -> NumericalColumn:
+    def as_numerical_column(self, dtype: Dtype) -> NumericalColumn:
         dtype = cudf.dtype(dtype)
         if dtype == self.dtype:
             return self
diff --git a/python/cudf/cudf/core/column/string.py b/python/cudf/cudf/core/column/string.py
index 06b5ac31ca6..84333fc205a 100644
--- a/python/cudf/cudf/core/column/string.py
+++ b/python/cudf/cudf/core/column/string.py
@@ -5633,7 +5633,7 @@ def __contains__(self, item: ScalarLike) -> bool:
             )
 
     def as_numerical_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.NumericalColumn":
         out_dtype = cudf.api.types.dtype(dtype)
         string_col = self
@@ -5696,14 +5696,13 @@ def _as_datetime_or_timedelta_column(self, dtype, format):
         return result_col
 
     def as_datetime_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.DatetimeColumn":
         out_dtype = cudf.api.types.dtype(dtype)
 
         # infer on host from the first not na element
         # or return all null column if all values
         # are null in current column
-        format = kwargs.get("format", None)
         if format is None:
             if self.null_count == len(self):
                 return cast(
@@ -5720,19 +5719,20 @@ def as_datetime_column(
         return self._as_datetime_or_timedelta_column(out_dtype, format)
 
     def as_timedelta_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.TimeDeltaColumn":
         out_dtype = cudf.api.types.dtype(dtype)
-        format = "%D days %H:%M:%S"
+        if format is None:
+            format = "%D days %H:%M:%S"
         return self._as_datetime_or_timedelta_column(out_dtype, format)
 
     def as_decimal_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.DecimalBaseColumn":
         return libstrings.to_decimal(self, dtype)
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> StringColumn:
         return self
 
diff --git a/python/cudf/cudf/core/column/timedelta.py b/python/cudf/cudf/core/column/timedelta.py
index 2f842130f48..6038a1a1e97 100644
--- a/python/cudf/cudf/core/column/timedelta.py
+++ b/python/cudf/cudf/core/column/timedelta.py
@@ -288,7 +288,7 @@ def fillna(
         return super().fillna(fill_value, method)
 
     def as_numerical_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype
     ) -> "cudf.core.column.NumericalColumn":
         col = column.build_column(
             data=self.base_data,
@@ -300,14 +300,14 @@ def as_numerical_column(
         return cast("cudf.core.column.NumericalColumn", col.astype(dtype))
 
     def as_datetime_column(
-        self, dtype: Dtype, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.DatetimeColumn":
         raise TypeError(
             f"cannot astype a timedelta from {self.dtype} to {dtype}"
         )
 
     def as_string_column(
-        self, dtype: Dtype, format=None, **kwargs
+        self, dtype: Dtype, format: str | None = None
     ) -> "cudf.core.column.StringColumn":
         if format is None:
             format = _dtype_to_format_conversion.get(
@@ -323,7 +323,9 @@ def as_string_column(
                 column.column_empty(0, dtype="object", masked=False),
             )
 
-    def as_timedelta_column(self, dtype: Dtype, **kwargs) -> TimeDeltaColumn:
+    def as_timedelta_column(
+        self, dtype: Dtype, format: str | None = None
+    ) -> TimeDeltaColumn:
         dtype = cudf.dtype(dtype)
         if dtype == self.dtype:
             return self
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index 813ecc32069..51b661593fc 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -24,6 +24,7 @@
     Set,
     Tuple,
     Union,
+    cast,
 )
 
 import cupy
@@ -1986,8 +1987,6 @@ def _make_operands_and_index_for_binop(
         fill_value: Any = None,
         reflect: bool = False,
         can_reindex: bool = False,
-        *args,
-        **kwargs,
     ) -> Tuple[
         Union[
             Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
@@ -2338,7 +2337,7 @@ def to_dict(
 
     @_cudf_nvtx_annotate
     def scatter_by_map(
-        self, map_index, map_size=None, keep_index=True, **kwargs
+        self, map_index, map_size=None, keep_index=True, debug: bool = False
     ):
         """Scatter to a list of dataframes.
 
@@ -2379,7 +2378,11 @@ def scatter_by_map(
 
         # Convert string or categorical to integer
         if isinstance(map_index, cudf.core.column.StringColumn):
-            map_index = map_index.as_categorical_column("category").codes
+            cat_index = cast(
+                cudf.core.column.CategoricalColumn,
+                map_index.as_categorical_column("category"),
+            )
+            map_index = cat_index.codes
             warnings.warn(
                 "Using StringColumn for map_index in scatter_by_map. "
                 "Use an integer array/column for better performance."
@@ -2391,7 +2394,7 @@ def scatter_by_map(
                 "Use an integer array/column for better performance."
             )
 
-        if kwargs.get("debug", False) == 1 and map_size is not None:
+        if debug and map_size is not None:
             count = map_index.distinct_count()
             if map_size < count:
                 raise ValueError(
@@ -2406,7 +2409,7 @@ def scatter_by_map(
         partitioned = self._from_columns_like_self(
             partitioned_columns,
             column_names=self._column_names,
-            index_names=self._index_names if keep_index else None,
+            index_names=list(self._index_names) if keep_index else None,
         )
 
         # due to the split limitation mentioned
@@ -2537,7 +2540,7 @@ def items(self):
             yield (k, self[k])
 
     @_cudf_nvtx_annotate
-    def equals(self, other, **kwargs):
+    def equals(self, other):
         ret = super().equals(other)
         # If all other checks matched, validate names.
         if ret:
diff --git a/python/cudf/cudf/core/indexed_frame.py b/python/cudf/cudf/core/indexed_frame.py
index ab089ceb103..5955e21fea0 100644
--- a/python/cudf/cudf/core/indexed_frame.py
+++ b/python/cudf/cudf/core/indexed_frame.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION.
 """Base class for Frame types that have an index."""
 
 from __future__ import annotations
@@ -3612,8 +3612,6 @@ def _make_operands_and_index_for_binop(
         fill_value: Any = None,
         reflect: bool = False,
         can_reindex: bool = False,
-        *args,
-        **kwargs,
     ) -> Tuple[
         Union[
             Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
diff --git a/python/cudf/cudf/core/series.py b/python/cudf/cudf/core/series.py
index 8739a61dd8b..df5a62b384e 100644
--- a/python/cudf/cudf/core/series.py
+++ b/python/cudf/cudf/core/series.py
@@ -1578,8 +1578,6 @@ def _make_operands_and_index_for_binop(
         fill_value: Any = None,
         reflect: bool = False,
         can_reindex: bool = False,
-        *args,
-        **kwargs,
     ) -> Tuple[
         Union[
             Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
diff --git a/python/cudf/cudf/core/single_column_frame.py b/python/cudf/cudf/core/single_column_frame.py
index 911e7ac905c..b73f756d7dc 100644
--- a/python/cudf/cudf/core/single_column_frame.py
+++ b/python/cudf/cudf/core/single_column_frame.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2023, NVIDIA CORPORATION.
+# Copyright (c) 2021-2024, NVIDIA CORPORATION.
 """Base class for Frame types that only have a single column."""
 
 from __future__ import annotations
@@ -310,8 +310,6 @@ def _make_operands_for_binop(
         other: Any,
         fill_value: Any = None,
         reflect: bool = False,
-        *args,
-        **kwargs,
     ) -> Union[
         Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
         NotImplementedType,