Move unnecessary utilities from cudf._lib.scalar

python/cudf/cudf/_lib/scalar.pyx

@@ -263,26 +263,3 @@ cdef class DeviceScalar:
             self._dtype = LIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
                 <underlying_type_t_type_id>(cdtype.id())
             ]
-
-
-def as_device_scalar(val, dtype=None):
-    if isinstance(val, (cudf.Scalar, DeviceScalar)):
-        if dtype == val.dtype or dtype is None:
-            if isinstance(val, DeviceScalar):
-                return val
-            else:
-                return val.device_value
-        else:
-            raise TypeError("Can't update dtype of existing GPU scalar")
-    else:
-        return cudf.Scalar(val, dtype=dtype).device_value
-
-
-def _is_null_host_scalar(slr):
-    if cudf.utils.utils.is_na_like(slr):
-        return True
-    elif (isinstance(slr, (np.datetime64, np.timedelta64)) and np.isnat(slr)) or \
-            slr is pd.NaT:
-        return True
-    else:
-        return False

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

@@ -621,7 +621,7 @@ def ordered(self) -> bool:
     def __setitem__(self, key, value):
         if cudf.api.types.is_scalar(
             value
-        ) and cudf._lib.scalar._is_null_host_scalar(value):
+        ) and cudf.utils.utils._is_null_host_scalar(value):
             to_add_categories = 0
         else:
             if cudf.api.types.is_scalar(value):

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

@@ -25,7 +25,6 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._lib.column import Column
-from cudf._lib.scalar import as_device_scalar
 from cudf._lib.types import dtype_to_pylibcudf_type, size_type_dtype
 from cudf.api.types import (
     _is_non_decimal_numeric_dtype,
@@ -71,7 +70,7 @@
     min_signed_type,
     min_unsigned_type,
 )
-from cudf.utils.utils import _array_ufunc, mask_dtype
+from cudf.utils.utils import _array_ufunc, _is_null_host_scalar, mask_dtype
 
 if TYPE_CHECKING:
     import builtins
@@ -777,9 +776,7 @@ def fillna(
         if not self.has_nulls(include_nan=True):
             return self.copy()
         elif method is None:
-            if is_scalar(fill_value) and libcudf.scalar._is_null_host_scalar(
-                fill_value
-            ):
+            if is_scalar(fill_value) and _is_null_host_scalar(fill_value):
                 return self.copy()
             else:
                 fill_value = self._validate_fillna_value(fill_value)
@@ -1984,12 +1981,12 @@ def as_column(
             column = Column.from_pylibcudf(
                 plc.filling.sequence(
                     len(arbitrary),
-                    as_device_scalar(
+                    cudf.Scalar(
                         arbitrary.start, dtype=np.dtype(np.int64)
-                    ).c_value,
-                    as_device_scalar(
+                    ).device_value.c_value,
+                    cudf.Scalar(
                         arbitrary.step, dtype=np.dtype(np.int64)
-                    ).c_value,
+                    ).device_value.c_value,
                 )
             )
         if cudf.get_option("default_integer_bitwidth") and dtype is None:

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

@@ -236,7 +236,7 @@ def from_sequences(
 
         # Build Data, Mask & Offsets
         for data in arbitrary:
-            if cudf._lib.scalar._is_null_host_scalar(data):
+            if cudf.utils.utils._is_null_host_scalar(data):
                 mask_col.append(False)
                 offset_vals.append(offset)
             else:

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

@@ -151,7 +151,7 @@ def __setitem__(self, key: Any, value: Any):
             cudf.Scalar(
                 value,
                 dtype=self.dtype
-                if cudf._lib.scalar._is_null_host_scalar(value)
+                if cudf.utils.utils._is_null_host_scalar(value)
                 else None,
             )
             if is_scalar(value)
@@ -789,7 +789,7 @@ def _normalize_find_and_replace_input(
         )
         # Scalar case
         if len(col_to_normalize) == 1:
-            if cudf._lib.scalar._is_null_host_scalar(col_to_normalize[0]):
+            if cudf.utils.utils._is_null_host_scalar(col_to_normalize[0]):
                 return normalized_column.astype(input_column_dtype)
             if np.isinf(col_to_normalize[0]):
                 return normalized_column

python/cudf/cudf/core/dataframe.py

@@ -92,7 +92,11 @@
     min_signed_type,
 )
 from cudf.utils.performance_tracking import _performance_tracking
-from cudf.utils.utils import GetAttrGetItemMixin, _external_only_api
+from cudf.utils.utils import (
+    GetAttrGetItemMixin,
+    _external_only_api,
+    _is_null_host_scalar,
+)
 
 if TYPE_CHECKING:
     from cudf._typing import ColumnLike, Dtype, NotImplementedType
@@ -3371,7 +3375,7 @@ def _insert(self, loc, name, value, nan_as_null=None, ignore_index=True):
             if isinstance(value, (np.ndarray, cupy.ndarray)):
                 dtype = value.dtype
                 value = value.item()
-            if libcudf.scalar._is_null_host_scalar(value):
+            if _is_null_host_scalar(value):
                 dtype = "str"
             value = as_column(
                 value,

python/cudf/cudf/core/scalar.py

@@ -178,13 +178,13 @@ def dtype(self):
     def is_valid(self):
         if not self._is_host_value_current:
             self._device_value_to_host()
-        return not cudf._lib.scalar._is_null_host_scalar(self._host_value)
+        return not cudf.utils.utils._is_null_host_scalar(self._host_value)
 
     def _device_value_to_host(self):
         self._host_value = self._device_value._to_host_scalar()
 
     def _preprocess_host_value(self, value, dtype):
-        valid = not cudf._lib.scalar._is_null_host_scalar(value)
+        valid = not cudf.utils.utils._is_null_host_scalar(value)
 
         if isinstance(value, list):
             if dtype is not None:

python/cudf/cudf/utils/dtypes.py

@@ -198,7 +198,7 @@ def to_cudf_compatible_scalar(val, dtype=None):
     If `val` is None, returns None.
     """
 
-    if cudf._lib.scalar._is_null_host_scalar(val) or isinstance(
+    if cudf.utils.utils._is_null_host_scalar(val) or isinstance(
         val, cudf.Scalar
     ):
         return val

python/cudf/cudf/utils/utils.py

@@ -341,6 +341,17 @@ def is_na_like(obj):
     return obj is None or obj is cudf.NA or obj is cudf.NaT
 
 
+def _is_null_host_scalar(slr) -> bool:
+    if is_na_like(slr):
+        return True
+    elif (
+        isinstance(slr, (np.datetime64, np.timedelta64)) and np.isnat(slr)
+    ) or slr is pd.NaT:
+        return True
+    else:
+        return False
+
+
 def _warn_no_dask_cudf(fn):
     @functools.wraps(fn)
     def wrapper(self):