diff --git a/python/cudf/cudf/core/accessors/lists.py b/python/cudf/cudf/core/accessors/lists.py
index 24207fd30ba..37d523863cb 100644
--- a/python/cudf/cudf/core/accessors/lists.py
+++ b/python/cudf/cudf/core/accessors/lists.py
@@ -11,8 +11,8 @@
 from cudf.api.types import is_scalar
 from cudf.core.accessors.base_accessor import BaseAccessor
 from cudf.core.column.column import as_column
+from cudf.core.dtype.validators import is_dtype_obj_list, is_dtype_obj_numeric
 from cudf.core.dtypes import ListDtype, dtype as cudf_dtype
-from cudf.utils.dtypes import is_dtype_obj_list, is_dtype_obj_numeric
 from cudf.utils.scalar import pa_scalar_to_plc_scalar
 
 if TYPE_CHECKING:
diff --git a/python/cudf/cudf/core/accessors/struct.py b/python/cudf/cudf/core/accessors/struct.py
index d5a2da272a4..3a6f6b6468c 100644
--- a/python/cudf/cudf/core/accessors/struct.py
+++ b/python/cudf/cudf/core/accessors/struct.py
@@ -7,8 +7,9 @@
 
 from cudf.core.accessors.base_accessor import BaseAccessor
 from cudf.core.column.struct import StructColumn
+from cudf.core.dtype.validators import is_dtype_obj_struct
 from cudf.core.dtypes import StructDtype
-from cudf.utils.dtypes import get_dtype_of_same_kind, is_dtype_obj_struct
+from cudf.utils.dtypes import get_dtype_of_same_kind
 
 if TYPE_CHECKING:
     from cudf.core.dataframe import DataFrame
diff --git a/python/cudf/cudf/core/column/categorical.py b/python/cudf/cudf/core/column/categorical.py
index 016720aa08f..1e1a372dd91 100644
--- a/python/cudf/cudf/core/column/categorical.py
+++ b/python/cudf/cudf/core/column/categorical.py
@@ -79,6 +79,15 @@ class CategoricalColumn(column.ColumnBase):
         plc.TypeId.UINT64,
     }
 
+    @classmethod
+    def _validate_args(  # type: ignore[override]
+        cls, plc_column: plc.Column, dtype: CategoricalDtype
+    ) -> tuple[plc.Column, CategoricalDtype]:
+        plc_column, dtype = super()._validate_args(plc_column, dtype)  # type: ignore[assignment]
+        if not isinstance(dtype, CategoricalDtype):
+            raise ValueError(f"{dtype=} must be a CategoricalDtype instance")
+        return plc_column, dtype
+
     def __contains__(self, item: ScalarLike) -> bool:
         try:
             encoded = self._encode(item)
diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 85a5ab1e8d8..cfa409afe60 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -50,6 +50,13 @@
 )
 from cudf.core.column.utils import access_columns
 from cudf.core.copy_types import GatherMap
+from cudf.core.dtype.validators import (
+    is_dtype_obj_decimal,
+    is_dtype_obj_interval,
+    is_dtype_obj_list,
+    is_dtype_obj_numeric,
+    is_dtype_obj_struct,
+)
 from cudf.core.dtypes import (
     CategoricalDtype,
     DecimalDtype,
@@ -72,11 +79,6 @@
     find_common_type,
     get_dtype_of_same_kind,
     is_column_like,
-    is_dtype_obj_decimal,
-    is_dtype_obj_interval,
-    is_dtype_obj_list,
-    is_dtype_obj_numeric,
-    is_dtype_obj_struct,
     is_mixed_with_object_dtype,
     is_pandas_nullable_extension_dtype,
     min_signed_type,
@@ -863,7 +865,7 @@ def _prep_pandas_compat_repr(self) -> StringColumn | Self:
         * null (other types)= str(pd.NA)
         """
         if self.has_nulls():
-            return self.astype(np.dtype("str")).fillna(
+            return self.astype(CUDF_STRING_DTYPE).fillna(
                 str(self._PANDAS_NA_VALUE)
             )
         return self
diff --git a/python/cudf/cudf/core/column/decimal.py b/python/cudf/cudf/core/column/decimal.py
index 1b216064de8..8b0b1404c37 100644
--- a/python/cudf/cudf/core/column/decimal.py
+++ b/python/cudf/cudf/core/column/decimal.py
@@ -5,7 +5,7 @@
 
 import warnings
 from decimal import Decimal
-from typing import TYPE_CHECKING, Any, Self, cast
+from typing import TYPE_CHECKING, Any, ClassVar, Self, cast
 
 import numpy as np
 import pandas as pd
@@ -19,14 +19,16 @@
 from cudf.core._internals import binaryop
 from cudf.core.column.column import ColumnBase, as_column
 from cudf.core.column.numerical_base import NumericalBaseColumn
+from cudf.core.dtype.validators import (
+    is_dtype_obj_decimal32,
+    is_dtype_obj_decimal64,
+    is_dtype_obj_decimal128,
+)
 from cudf.core.dtypes import (
     Decimal32Dtype,
     Decimal64Dtype,
     Decimal128Dtype,
     DecimalDtype,
-    is_decimal32_dtype,
-    is_decimal64_dtype,
-    is_decimal128_dtype,
 )
 from cudf.core.mixins import BinaryOperand
 from cudf.utils.dtypes import (
@@ -38,7 +40,7 @@
 from cudf.utils.utils import is_na_like
 
 if TYPE_CHECKING:
-    from collections.abc import Mapping
+    from collections.abc import Callable, Mapping
 
     from cudf._typing import (
         ColumnBinaryOperand,
@@ -70,14 +72,17 @@ class DecimalBaseColumn(NumericalBaseColumn):
     """Base column for decimal32, decimal64 or decimal128 columns"""
 
     _VALID_BINARY_OPERATIONS = BinaryOperand._SUPPORTED_BINARY_OPERATIONS
+    _decimal_type_check: ClassVar[Callable[[DtypeObj], bool]]
 
     @classmethod
     def _validate_args(  # type: ignore[override]
         cls, plc_column: plc.Column, dtype: DecimalDtype
     ) -> tuple[plc.Column, DecimalDtype]:
         plc_column, dtype = super()._validate_args(plc_column, dtype)  # type: ignore[assignment]
-        if not cls._decimal_check(dtype):  # type: ignore[attr-defined]
-            raise ValueError(f"{dtype=} must be a Decimal128Dtype instance")
+        if not cls._decimal_type_check(dtype):
+            raise ValueError(
+                f"{dtype=} must be a valid decimal dtype instance"
+            )
         return plc_column, dtype
 
     def _with_type_metadata(self: Self, dtype: DtypeObj) -> Self:
@@ -379,19 +384,19 @@ def to_pandas(
 class Decimal32Column(DecimalBaseColumn):
     _VALID_PLC_TYPES = {plc.TypeId.DECIMAL32}
     _decimal_cls = Decimal32Dtype
-    _decimal_check = is_decimal32_dtype
+    _decimal_type_check = is_dtype_obj_decimal32
 
 
 class Decimal64Column(DecimalBaseColumn):
     _VALID_PLC_TYPES = {plc.TypeId.DECIMAL64}
     _decimal_cls = Decimal64Dtype
-    _decimal_check = is_decimal64_dtype
+    _decimal_type_check = is_dtype_obj_decimal64
 
 
 class Decimal128Column(DecimalBaseColumn):
     _VALID_PLC_TYPES = {plc.TypeId.DECIMAL128}
     _decimal_cls = Decimal128Dtype
-    _decimal_check = is_decimal128_dtype
+    _decimal_type_check = is_dtype_obj_decimal128
 
     def to_arrow(self) -> pa.Array:
         arrow_array = super().to_arrow()
diff --git a/python/cudf/cudf/core/column/interval.py b/python/cudf/cudf/core/column/interval.py
index 056cf91d5b1..c352e6bc2f3 100644
--- a/python/cudf/cudf/core/column/interval.py
+++ b/python/cudf/cudf/core/column/interval.py
@@ -13,8 +13,8 @@
 
 import cudf
 from cudf.core.column.column import ColumnBase, _handle_nulls, as_column
+from cudf.core.dtype.validators import is_dtype_obj_interval
 from cudf.core.dtypes import IntervalDtype, _dtype_to_metadata
-from cudf.utils.dtypes import is_dtype_obj_interval
 from cudf.utils.scalar import maybe_nested_pa_scalar_to_py
 
 if TYPE_CHECKING:
diff --git a/python/cudf/cudf/core/column/lists.py b/python/cudf/cudf/core/column/lists.py
index 6e9663f7435..d38f20ec9cb 100644
--- a/python/cudf/cudf/core/column/lists.py
+++ b/python/cudf/cudf/core/column/lists.py
@@ -15,12 +15,10 @@
 
 import cudf
 from cudf.core.column.column import ColumnBase, as_column, column_empty
+from cudf.core.dtype.validators import is_dtype_obj_list
 from cudf.core.dtypes import ListDtype
 from cudf.core.missing import NA
-from cudf.utils.dtypes import (
-    get_dtype_of_same_kind,
-    is_dtype_obj_list,
-)
+from cudf.utils.dtypes import get_dtype_of_same_kind
 from cudf.utils.scalar import (
     maybe_nested_pa_scalar_to_py,
     pa_scalar_to_plc_scalar,
diff --git a/python/cudf/cudf/core/column/string.py b/python/cudf/cudf/core/column/string.py
index 29757c38a12..c9ea4775276 100644
--- a/python/cudf/cudf/core/column/string.py
+++ b/python/cudf/cudf/core/column/string.py
@@ -19,14 +19,13 @@
 from cudf.api.types import is_scalar
 from cudf.core._internals import binaryop
 from cudf.core.column.column import ColumnBase, as_column, column_empty
+from cudf.core.dtype.validators import is_dtype_obj_string
 from cudf.core.mixins import Scannable
 from cudf.errors import MixedTypeError
 from cudf.utils.dtypes import (
-    CUDF_STRING_DTYPE,
     cudf_dtype_to_pa_type,
     dtype_to_pylibcudf_type,
     get_dtype_of_same_kind,
-    is_dtype_obj_string,
     is_pandas_nullable_extension_dtype,
 )
 from cudf.utils.scalar import pa_scalar_to_plc_scalar
@@ -117,21 +116,8 @@ def _validate_args(
         cls, plc_column: plc.Column, dtype: np.dtype
     ) -> tuple[plc.Column, np.dtype]:
         plc_column, dtype = super()._validate_args(plc_column, dtype)
-        if (
-            not cudf.get_option("mode.pandas_compatible")
-            and dtype != CUDF_STRING_DTYPE
-            and dtype.kind != "U"
-        ) or (
-            cudf.get_option("mode.pandas_compatible")
-            and not is_dtype_obj_string(dtype)
-        ):
-            raise ValueError(f"dtype must be {CUDF_STRING_DTYPE}")
-        if (
-            cudf.get_option("mode.pandas_compatible")
-            and isinstance(dtype, np.dtype)
-            and dtype.kind == "U"
-        ):
-            dtype = CUDF_STRING_DTYPE
+        if not is_dtype_obj_string(dtype):
+            raise ValueError("dtype must be a valid cuDF string dtype")
         return plc_column, dtype
 
     @property
diff --git a/python/cudf/cudf/core/column/struct.py b/python/cudf/cudf/core/column/struct.py
index 2aff7c1051e..ca0c0df69f6 100644
--- a/python/cudf/cudf/core/column/struct.py
+++ b/python/cudf/cudf/core/column/struct.py
@@ -11,11 +11,9 @@
 
 import cudf
 from cudf.core.column.column import ColumnBase
+from cudf.core.dtype.validators import is_dtype_obj_struct
 from cudf.core.dtypes import StructDtype
-from cudf.utils.dtypes import (
-    dtype_from_pylibcudf_column,
-    is_dtype_obj_struct,
-)
+from cudf.utils.dtypes import dtype_from_pylibcudf_column
 from cudf.utils.scalar import (
     maybe_nested_pa_scalar_to_py,
     pa_scalar_to_plc_scalar,
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index 6f74878a8c5..aaae6eaf051 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -62,6 +62,7 @@
 )
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.copy_types import BooleanMask
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import (
     CategoricalDtype,
     Decimal32Dtype,
@@ -107,7 +108,6 @@
     find_common_type,
     get_dtype_of_same_kind,
     is_column_like,
-    is_dtype_obj_numeric,
     is_mixed_with_object_dtype,
     is_pandas_nullable_extension_dtype,
     min_signed_type,
diff --git a/python/cudf/cudf/core/dtype/__init__.py b/python/cudf/cudf/core/dtype/__init__.py
new file mode 100644
index 00000000000..8eca3cc68ad
--- /dev/null
+++ b/python/cudf/cudf/core/dtype/__init__.py
@@ -0,0 +1,2 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+# SPDX-License-Identifier: Apache-2.0
diff --git a/python/cudf/cudf/core/dtype/validators.py b/python/cudf/cudf/core/dtype/validators.py
new file mode 100644
index 00000000000..1e5abcb6598
--- /dev/null
+++ b/python/cudf/cudf/core/dtype/validators.py
@@ -0,0 +1,198 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION.
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pandas as pd
+import pyarrow as pa
+
+import cudf
+from cudf.utils.dtypes import CUDF_STRING_DTYPE
+
+if TYPE_CHECKING:
+    from cudf._typing import DtypeObj
+
+
+def is_dtype_obj_string(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF string type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF string type.
+    """
+    return (
+        obj == CUDF_STRING_DTYPE
+        or isinstance(obj, pd.StringDtype)
+        or (
+            isinstance(obj, pd.ArrowDtype)
+            and (
+                pa.types.is_string(obj.pyarrow_dtype)
+                or pa.types.is_large_string(obj.pyarrow_dtype)
+            )
+        )
+    )
+
+
+def is_dtype_obj_list(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF list type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF list type.
+    """
+    return isinstance(obj, cudf.ListDtype) or (
+        isinstance(obj, pd.ArrowDtype) and pa.types.is_list(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_struct(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF struct type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF struct type.
+    """
+    return isinstance(obj, cudf.StructDtype) or (
+        isinstance(obj, pd.ArrowDtype)
+        and pa.types.is_struct(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_interval(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF interval type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF interval type.
+    """
+    return isinstance(obj, cudf.IntervalDtype) or (
+        isinstance(obj, pd.ArrowDtype)
+        and pa.types.is_interval(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_decimal(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF decimal type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF decimal type.
+    """
+    return (
+        is_dtype_obj_decimal32(obj)
+        or is_dtype_obj_decimal64(obj)
+        or is_dtype_obj_decimal128(obj)
+    )
+
+
+def is_dtype_obj_decimal32(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF decimal32 type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF decimal32 type.
+    """
+    return isinstance(obj, cudf.Decimal32Dtype) or (
+        isinstance(obj, pd.ArrowDtype)
+        and pa.types.is_decimal32(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_decimal64(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF decimal64 type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF decimal64 type.
+    """
+    return isinstance(obj, cudf.Decimal64Dtype) or (
+        isinstance(obj, pd.ArrowDtype)
+        and pa.types.is_decimal64(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_decimal128(obj: DtypeObj) -> bool:
+    """Check whether the provided dtype object is a cuDF decimal128 type.
+
+    Parameters
+    ----------
+    obj : DtypeObj
+        The dtype object to check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a cuDF decimal128 type.
+    """
+    return isinstance(obj, cudf.Decimal128Dtype) or (
+        isinstance(obj, pd.ArrowDtype)
+        and pa.types.is_decimal128(obj.pyarrow_dtype)
+    )
+
+
+def is_dtype_obj_numeric(
+    dtype: DtypeObj, include_decimal: bool = True
+) -> bool:
+    """
+    Check whether the provided dtype object is a numeric type.
+
+    Parameters
+    ----------
+    dtype: DtypeObj
+        The dtype object to check.
+    include_decimal: bool, default True
+        Whether to include decimal types in the check.
+
+    Returns
+    -------
+    bool
+        Whether or not the dtype object is a numeric type.
+    """
+    is_non_decimal = dtype.kind in set("iufb")
+    if include_decimal:
+        return is_non_decimal or is_dtype_obj_decimal(dtype)
+    else:
+        return is_non_decimal
diff --git a/python/cudf/cudf/core/dtypes.py b/python/cudf/cudf/core/dtypes.py
index e3295707601..2a47ad20261 100644
--- a/python/cudf/cudf/core/dtypes.py
+++ b/python/cudf/cudf/core/dtypes.py
@@ -21,6 +21,7 @@
 import cudf
 from cudf.core._compat import PANDAS_GE_210, PANDAS_LT_300
 from cudf.core.abc import Serializable
+from cudf.core.dtype.validators import is_dtype_obj_string
 from cudf.utils.docutils import doc_apply
 from cudf.utils.dtypes import (
     CUDF_STRING_DTYPE,
@@ -995,9 +996,13 @@ def __init__(
             self._fields = {}
         else:
             self._subtype = cudf.dtype(subtype)
-            if isinstance(
-                self._subtype, cudf.CategoricalDtype
-            ) or cudf.utils.dtypes.is_dtype_obj_string(self._subtype):
+            # TODO: Remove self._subtype.kind == "U" once cudf.dtype no longer accepts
+            # numpy string types
+            if (
+                isinstance(self._subtype, CategoricalDtype)
+                or is_dtype_obj_string(self._subtype)
+                or self._subtype.kind == "U"
+            ):
                 raise TypeError(
                     "category, object, and string subtypes are not supported "
                     "for IntervalDtype"
diff --git a/python/cudf/cudf/core/frame.py b/python/cudf/cudf/core/frame.py
index a16ea1d57d7..0367150e260 100644
--- a/python/cudf/cudf/core/frame.py
+++ b/python/cudf/cudf/core/frame.py
@@ -33,10 +33,10 @@
     serialize_columns,
 )
 from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.mixins import BinaryOperand, Scannable
 from cudf.utils.dtypes import (
     find_common_type,
-    is_dtype_obj_numeric,
     is_pandas_nullable_extension_dtype,
 )
 from cudf.utils.performance_tracking import _performance_tracking
diff --git a/python/cudf/cudf/core/groupby/groupby.py b/python/cudf/cudf/core/groupby/groupby.py
index 459563912e6..f852c426c63 100644
--- a/python/cudf/cudf/core/groupby/groupby.py
+++ b/python/cudf/cudf/core/groupby/groupby.py
@@ -34,6 +34,7 @@
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.common import pipe
 from cudf.core.copy_types import GatherMap
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import (
     CategoricalDtype,
     DecimalDtype,
@@ -53,7 +54,6 @@
     SIZE_TYPE_DTYPE,
     cudf_dtype_to_pa_type,
     get_dtype_of_same_kind,
-    is_dtype_obj_numeric,
 )
 from cudf.utils.performance_tracking import _performance_tracking
 from cudf.utils.scalar import pa_scalar_to_plc_scalar
diff --git a/python/cudf/cudf/core/index.py b/python/cudf/cudf/core/index.py
index e58e384dec7..a7c3115e083 100644
--- a/python/cudf/cudf/core/index.py
+++ b/python/cudf/cudf/core/index.py
@@ -43,6 +43,7 @@
 from cudf.core.column.column import as_column, column_empty, concat_columns
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.copy_types import GatherMap
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import IntervalDtype, dtype as cudf_dtype
 from cudf.core.join._join_helpers import _match_join_keys
 from cudf.core.single_column_frame import SingleColumnFrame
@@ -56,7 +57,6 @@
     cudf_dtype_to_pa_type,
     dtype_to_pylibcudf_type,
     find_common_type,
-    is_dtype_obj_numeric,
     is_mixed_with_object_dtype,
 )
 from cudf.utils.performance_tracking import _performance_tracking
diff --git a/python/cudf/cudf/core/indexed_frame.py b/python/cudf/cudf/core/indexed_frame.py
index 0846f18547e..4ceb04586e2 100644
--- a/python/cudf/cudf/core/indexed_frame.py
+++ b/python/cudf/cudf/core/indexed_frame.py
@@ -51,6 +51,7 @@
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.core.common import pipe
 from cudf.core.copy_types import BooleanMask, GatherMap
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import ListDtype
 from cudf.core.frame import Frame
 from cudf.core.groupby.groupby import GroupBy
@@ -75,7 +76,6 @@
     find_common_type,
     get_dtype_of_same_kind,
     is_column_like,
-    is_dtype_obj_numeric,
     is_mixed_with_object_dtype,
     is_pandas_nullable_extension_dtype,
 )
diff --git a/python/cudf/cudf/core/join/_join_helpers.py b/python/cudf/cudf/core/join/_join_helpers.py
index e7e0147ff5f..aaedb410616 100644
--- a/python/cudf/cudf/core/join/_join_helpers.py
+++ b/python/cudf/cudf/core/join/_join_helpers.py
@@ -10,6 +10,7 @@
 import numpy as np
 
 from cudf.api.types import is_dtype_equal
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import (
     CategoricalDtype,
     Decimal32Dtype,
@@ -20,7 +21,6 @@
 from cudf.utils.dtypes import (
     find_common_type,
     get_dtype_of_same_kind,
-    is_dtype_obj_numeric,
 )
 
 if TYPE_CHECKING:
diff --git a/python/cudf/cudf/core/multiindex.py b/python/cudf/cudf/core/multiindex.py
index 207c92773ac..6515382b9c9 100644
--- a/python/cudf/cudf/core/multiindex.py
+++ b/python/cudf/cudf/core/multiindex.py
@@ -25,6 +25,7 @@
 from cudf.core.column import access_columns
 from cudf.core.column.column import ColumnBase
 from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.frame import Frame
 from cudf.core.index import (
     Index,
@@ -39,7 +40,6 @@
     CUDF_STRING_DTYPE,
     SIZE_TYPE_DTYPE,
     is_column_like,
-    is_dtype_obj_numeric,
     is_pandas_nullable_extension_dtype,
 )
 from cudf.utils.performance_tracking import _performance_tracking
diff --git a/python/cudf/cudf/core/series.py b/python/cudf/cudf/core/series.py
index 36ac5f3b5a3..3e80f476d8c 100644
--- a/python/cudf/cudf/core/series.py
+++ b/python/cudf/cudf/core/series.py
@@ -41,6 +41,7 @@
 )
 from cudf.core.column.column import concat_columns
 from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import CategoricalDtype, IntervalDtype
 from cudf.core.groupby.groupby import SeriesGroupBy, groupby_doc_template
 from cudf.core.index import (
@@ -65,7 +66,6 @@
     _get_nan_for_dtype,
     find_common_type,
     get_dtype_of_same_kind,
-    is_dtype_obj_numeric,
     is_mixed_with_object_dtype,
     is_pandas_nullable_extension_dtype,
 )
diff --git a/python/cudf/cudf/core/single_column_frame.py b/python/cudf/cudf/core/single_column_frame.py
index 65d8eb0c2da..242a7853044 100644
--- a/python/cudf/cudf/core/single_column_frame.py
+++ b/python/cudf/cudf/core/single_column_frame.py
@@ -16,9 +16,10 @@
 )
 from cudf.core.column import ColumnBase, as_column, column_empty
 from cudf.core.column_accessor import ColumnAccessor
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.frame import Frame
 from cudf.core.mixins import NotIterable
-from cudf.utils.dtypes import SIZE_TYPE_DTYPE, is_dtype_obj_numeric
+from cudf.utils.dtypes import SIZE_TYPE_DTYPE
 from cudf.utils.performance_tracking import _performance_tracking
 from cudf.utils.utils import _is_same_name
 
diff --git a/python/cudf/cudf/core/tools/numeric.py b/python/cudf/cudf/core/tools/numeric.py
index 2ddc651e1d6..93977e12e95 100644
--- a/python/cudf/cudf/core/tools/numeric.py
+++ b/python/cudf/cudf/core/tools/numeric.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2018-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2018-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 from __future__ import annotations
 
@@ -9,13 +9,13 @@
 import pandas as pd
 
 from cudf.core.column import as_column
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.dtypes import CategoricalDtype, ListDtype, StructDtype
 from cudf.core.index import ensure_index
 from cudf.core.series import Series
 from cudf.utils.dtypes import (
     CUDF_STRING_DTYPE,
     can_convert_to_column,
-    is_dtype_obj_numeric,
 )
 
 if TYPE_CHECKING:
diff --git a/python/cudf/cudf/core/window/ewm.py b/python/cudf/cudf/core/window/ewm.py
index 330216c2b9b..4d64a317f27 100644
--- a/python/cudf/cudf/core/window/ewm.py
+++ b/python/cudf/cudf/core/window/ewm.py
@@ -7,8 +7,8 @@
 
 import numpy as np
 
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.window.rolling import _RollingBase
-from cudf.utils.dtypes import is_dtype_obj_numeric
 
 if TYPE_CHECKING:
     from cudf.core.column.column import ColumnBase
diff --git a/python/cudf/cudf/pandas/scripts/conftest-patch.py b/python/cudf/cudf/pandas/scripts/conftest-patch.py
index 6d1f904121f..1ac6f6700f6 100644
--- a/python/cudf/cudf/pandas/scripts/conftest-patch.py
+++ b/python/cudf/cudf/pandas/scripts/conftest-patch.py
@@ -544,7 +544,6 @@ def set_copy_on_write_option():
     "tests/arrays/floating/test_construction.py::test_floating_array_constructor_copy",
     "tests/arrays/floating/test_function.py::test_ufuncs_single[sign]",
     "tests/arrays/floating/test_function.py::test_value_counts_empty",
-    "tests/arrays/floating/test_repr.py::test_frame_repr[Float32Dtype]",
     "tests/arrays/floating/test_to_numpy.py::test_to_numpy_copy",
     "tests/arrays/integer/test_arithmetic.py::test_values_multiplying_large_series_by_NA",
     "tests/arrays/integer/test_comparison.py::TestComparisonOps::test_ufunc_with_out[Int16Dtype]",
@@ -1432,7 +1431,6 @@ def set_copy_on_write_option():
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_construction_from_string",
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_construction_generic[Interval]",
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_construction_generic[interval]",
-    "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_construction_not_supported[<U10]",
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_equality",
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_hash_vs_equality",
     "tests/dtypes/test_dtypes.py::TestIntervalDtype::test_name_repr_generic[Interval]",
@@ -4987,7 +4985,6 @@ def set_copy_on_write_option():
     "tests/io/formats/style/test_style.py::test_copy[True-False-True]",
     "tests/io/formats/style/test_style.py::test_copy[True-True-False]",
     "tests/io/formats/style/test_style.py::test_copy[True-True-True]",
-    "tests/io/formats/test_eng_formatting.py::TestEngFormatter::test_nan",
     "tests/io/formats/test_format.py::TestDataFrameFormatting::test_info_repr",
     "tests/io/formats/test_format.py::TestDataFrameFormatting::test_info_repr_max_cols",
     "tests/io/formats/test_format.py::TestSeriesFormatting::test_east_asian_unicode_series",
@@ -6364,8 +6361,6 @@ def set_copy_on_write_option():
     "tests/series/methods/test_astype.py::TestAstype::test_astype_float_to_uint_negatives_raise[float64-uint64]",
     "tests/series/methods/test_astype.py::TestAstype::test_astype_float_to_uint_negatives_raise[float64-uint8]",
     "tests/series/methods/test_astype.py::TestAstype::test_astype_from_float_to_str[float32]",
-    "tests/series/methods/test_astype.py::TestAstype::test_astype_str_cast_td64",
-    "tests/series/methods/test_astype.py::TestAstype::test_astype_to_str_preserves_na[None-None]",
     "tests/series/methods/test_astype.py::TestAstypeCategorical::test_astype_categorical_to_other",
     "tests/series/methods/test_astype.py::TestAstypeString::test_astype_string_to_extension_dtype_roundtrip[string[pyarrow]-data1-category]",
     "tests/series/methods/test_astype.py::TestAstypeString::test_astype_string_to_extension_dtype_roundtrip[string[python]-data1-category]",
@@ -6841,7 +6836,6 @@ def set_copy_on_write_option():
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_numeric_column_with_na[0.5]",
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_numeric_column_with_na[0]",
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_string_dtype",
-    "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_string_dtype_list_to_replace",
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_value_none_dtype_numeric[2.0]",
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_value_none_dtype_numeric[2]",
     "tests/series/methods/test_replace.py::TestSeriesReplace::test_replace_value_none_dtype_numeric[nan]",
@@ -6995,7 +6989,6 @@ def set_copy_on_write_option():
     "tests/series/test_constructors.py::TestSeriesConstructors::test_series_string_inference",
     "tests/series/test_constructors.py::TestSeriesConstructors::test_series_string_inference_array_string_dtype",
     "tests/series/test_constructors.py::TestSeriesConstructors::test_series_string_inference_scalar",
-    "tests/series/test_constructors.py::TestSeriesConstructors::test_series_string_inference_storage_definition",
     "tests/series/test_constructors.py::TestSeriesConstructors::test_series_string_with_na_inference[None]",
     "tests/series/test_formats.py::TestCategoricalRepr::test_categorical_series_repr_datetime",
     "tests/series/test_formats.py::TestCategoricalRepr::test_categorical_series_repr_datetime_ordered",
diff --git a/python/cudf/cudf/testing/testing.py b/python/cudf/cudf/testing/testing.py
index 84f66a150b5..6b6d1c34610 100644
--- a/python/cudf/cudf/testing/testing.py
+++ b/python/cudf/cudf/testing/testing.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2020-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2020-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 from __future__ import annotations
@@ -13,8 +13,9 @@
 from pandas import testing as tm
 
 import cudf
+from cudf.core.dtype.validators import is_dtype_obj_numeric
 from cudf.core.missing import NA, NaT
-from cudf.utils.dtypes import CUDF_STRING_DTYPE, is_dtype_obj_numeric
+from cudf.utils.dtypes import CUDF_STRING_DTYPE
 
 pa_types = pa.types
 
diff --git a/python/cudf/cudf/utils/dtypes.py b/python/cudf/cudf/utils/dtypes.py
index 639f6924619..18bacc19d93 100644
--- a/python/cudf/cudf/utils/dtypes.py
+++ b/python/cudf/cudf/utils/dtypes.py
@@ -2,13 +2,11 @@
 # SPDX-License-Identifier: Apache-2.0
 from __future__ import annotations
 
-import warnings
 from typing import TYPE_CHECKING, Any, TypeGuard
 
 import numpy as np
 import pandas as pd
 import pyarrow as pa
-from pandas.api import types as pd_types  # noqa: TID251
 from pandas.core.computation.common import result_type_many
 
 import pylibcudf as plc
@@ -326,6 +324,8 @@ def find_common_type(dtypes: Iterable[DtypeObj]) -> DtypeObj | None:
     if any(
         isinstance(dtype, cudf.core.dtypes.DecimalDtype) for dtype in dtypes
     ):
+        from cudf.core.dtype.validators import is_dtype_obj_numeric
+
         if all(
             is_dtype_obj_numeric(dtype, include_decimal=True)
             for dtype in dtypes
@@ -390,20 +390,6 @@ def _get_base_dtype(dtype: pd.DatetimeTZDtype) -> np.dtype:
         return dtype.base
 
 
-def is_dtype_obj_numeric(
-    dtype: DtypeObj, include_decimal: bool = True
-) -> bool:
-    """Like is_numeric_dtype but does not introspect argument."""
-    is_non_decimal = dtype.kind in set("iufb")
-    if include_decimal:
-        return is_non_decimal or isinstance(
-            dtype,
-            (cudf.Decimal32Dtype, cudf.Decimal64Dtype, cudf.Decimal128Dtype),
-        )
-    else:
-        return is_non_decimal
-
-
 def pyarrow_dtype_to_cudf_dtype(dtype: pd.ArrowDtype) -> DtypeObj:
     """Given a pandas ArrowDtype, converts it into the equivalent cudf pandas
     dtype.
@@ -602,168 +588,6 @@ def dtype_from_pylibcudf_column(col: plc.Column) -> DtypeObj:
         return PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[tid]
 
 
-def is_dtype_obj_categorical(obj):
-    if obj is None:
-        return False
-
-    if isinstance(
-        obj,
-        (
-            pd.CategoricalDtype,
-            cudf.CategoricalDtype,
-        ),
-    ):
-        return True
-
-    if any(
-        obj is t
-        for t in (
-            cudf.CategoricalDtype,
-            pd.CategoricalDtype,
-            pd.CategoricalDtype.type,
-        )
-    ):
-        return True
-    if isinstance(obj, str) and obj == "category":
-        return True
-
-    # TODO: A lot of the above checks are probably redundant and should be
-    # farmed out to this function here instead.
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        return pd_types.is_categorical_dtype(obj)
-
-
-def is_dtype_obj_string(obj):
-    """Check whether the provided array or dtype is of the string dtype.
-
-    Parameters
-    ----------
-    obj : array-like or dtype
-        The array or dtype to check.
-
-    Returns
-    -------
-    bool
-        Whether or not the array or dtype is of the string dtype.
-    """
-    return (
-        obj is CUDF_STRING_DTYPE
-        or obj is np.dtype("str")
-        or (isinstance(obj, pd.StringDtype))
-        or (
-            isinstance(obj, pd.ArrowDtype)
-            and (
-                pa.types.is_string(obj.pyarrow_dtype)
-                or pa.types.is_large_string(obj.pyarrow_dtype)
-            )
-        )
-    )
-
-
-def is_dtype_obj_list(obj):
-    """Check whether an array-like or dtype is of the list dtype.
-
-    Parameters
-    ----------
-    obj : array-like or dtype
-        The array-like or dtype to check.
-
-    Returns
-    -------
-    bool
-        Whether or not the array-like or dtype is of the list dtype.
-    """
-    return type(obj) is cudf.ListDtype or (
-        isinstance(obj, pd.ArrowDtype) and pa.types.is_list(obj.pyarrow_dtype)
-    )
-
-
-def is_dtype_obj_struct(obj):
-    """Check whether an array-like or dtype is of the struct dtype.
-
-    Parameters
-    ----------
-    obj : array-like or dtype
-        The array-like or dtype to check.
-
-    Returns
-    -------
-    bool
-        Whether or not the array-like or dtype is of the struct dtype.
-    """
-    # TODO: This behavior is currently inconsistent for interval types. the
-    # actual class IntervalDtype will return False, but instances (e.g.
-    # IntervalDtype(int)) will return True. For now this is not being changed
-    # since the interval dtype is being modified as part of the array refactor,
-    # but this behavior should be made consistent afterwards.
-    return isinstance(obj, cudf.StructDtype) or (
-        isinstance(obj, pd.ArrowDtype)
-        and pa.types.is_struct(obj.pyarrow_dtype)
-    )
-
-
-def is_dtype_obj_interval(obj):
-    return isinstance(
-        obj,
-        (
-            cudf.IntervalDtype,
-            pd.IntervalDtype,
-        ),
-    ) or (
-        isinstance(obj, pd.ArrowDtype)
-        and pa.types.is_interval(obj.pyarrow_dtype)
-    )
-
-
-def is_dtype_obj_decimal(obj):
-    """Check whether an array-like or dtype is of the decimal dtype.
-
-    Parameters
-    ----------
-    obj : array-like or dtype
-        The array-like or dtype to check.
-
-    Returns
-    -------
-    bool
-        Whether or not the array-like or dtype is of the decimal dtype.
-    """
-    return (
-        is_dtype_obj_decimal32(obj)
-        or is_dtype_obj_decimal64(obj)
-        or is_dtype_obj_decimal128(obj)
-    )
-
-
-def is_dtype_obj_decimal32(obj):
-    return (
-        type(obj) is cudf.Decimal32Dtype
-        or obj is cudf.Decimal32Dtype
-        or (isinstance(obj, str) and obj == cudf.Decimal32Dtype.name)
-    )
-
-
-def is_dtype_obj_decimal64(obj):
-    return (
-        type(obj) is cudf.Decimal64Dtype
-        or obj is cudf.Decimal64Dtype
-        or (isinstance(obj, str) and obj == cudf.Decimal64Dtype.name)
-    )
-
-
-def is_dtype_obj_decimal128(obj):
-    return (
-        type(obj) is cudf.Decimal128Dtype
-        or obj is cudf.Decimal128Dtype
-        or (isinstance(obj, str) and obj == cudf.Decimal128Dtype.name)
-        or (
-            isinstance(obj, pd.ArrowDtype)
-            and pa.types.is_decimal128(obj.pyarrow_dtype)
-        )
-    )
-
-
 SUPPORTED_NUMPY_TO_PYLIBCUDF_TYPES: dict[np.dtype[Any], plc.types.TypeId] = {
     np.dtype("int8"): plc.types.TypeId.INT8,
     np.dtype("int16"): plc.types.TypeId.INT16,