Replace _is_datetime64tz/interval_dtype with isinstance

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

@@ -53,8 +53,6 @@
 from cudf._typing import ColumnLike, Dtype, ScalarLike
 from cudf.api.types import (
     _is_categorical_dtype,
-    _is_datetime64tz_dtype,
-    _is_interval_dtype,
     _is_non_decimal_numeric_dtype,
     _is_pandas_nullable_extension_dtype,
     infer_dtype,
@@ -2263,9 +2261,17 @@ def as_column(
         np_type = None
         try:
             if dtype is not None:
-                if _is_categorical_dtype(dtype) or _is_interval_dtype(dtype):
+                if dtype in {"category", "interval"} or isinstance(
+                    dtype,
+                    (
+                        cudf.CategoricalDtype,
+                        cudf.IntervalDtype,
+                        pd.IntervalDtype,
+                        pd.CategoricalDtype,
+                    ),
+                ):
                     raise TypeError
-                if _is_datetime64tz_dtype(dtype):
+                if isinstance(dtype, pd.DatetimeTZDtype):
                     raise NotImplementedError(
                         "Use `tz_localize()` to construct "
                         "timezone aware data."
@@ -2413,7 +2419,9 @@ def as_column(
             elif np_type == np.str_:
                 sr = pd.Series(arbitrary, dtype="str")
                 data = as_column(sr, nan_as_null=nan_as_null)
-            elif _is_interval_dtype(dtype):
+            elif dtype == "interval" or isinstance(
+                dtype, (pd.IntervalDtype, cudf.IntervalDtype)
+            ):
                 sr = pd.Series(arbitrary, dtype="interval")
                 data = as_column(sr, nan_as_null=nan_as_null, dtype=dtype)
             elif (

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

@@ -22,12 +22,7 @@
     DtypeObj,
     ScalarLike,
 )
-from cudf.api.types import (
-    _is_datetime64tz_dtype,
-    is_datetime64_dtype,
-    is_scalar,
-    is_timedelta64_dtype,
-)
+from cudf.api.types import is_datetime64_dtype, is_scalar, is_timedelta64_dtype
 from cudf.core._compat import PANDAS_GE_200, PANDAS_GE_220
 from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
 from cudf.core.column import ColumnBase, as_column, column, string
@@ -702,7 +697,7 @@ def can_cast_safely(self, to_dtype: Dtype) -> bool:
             return False
 
     def _with_type_metadata(self, dtype):
-        if _is_datetime64tz_dtype(dtype):
+        if isinstance(dtype, pd.DatetimeTZDtype):
             return DatetimeTZColumn(
                 data=self.base_data,
                 dtype=dtype,

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

@@ -5,7 +5,6 @@
 import pyarrow as pa
 
 import cudf
-from cudf.api.types import _is_interval_dtype
 from cudf.core.column import StructColumn
 from cudf.core.dtypes import CategoricalDtype, IntervalDtype
 
@@ -94,20 +93,13 @@ def as_interval_column(self, dtype):
                 new_struct = self._get_decategorized_column()
                 return IntervalColumn.from_struct_column(new_struct)
             else:
-                # a user can directly input the string `interval` as the dtype
-                # when creating an interval series or interval dataframe
-                if _is_interval_dtype(dtype):
-                    dtype = IntervalDtype(
-                        self.dtype.subtype, self.dtype.closed
-                    )
-                children = self.children
                 return IntervalColumn(
                     size=self.size,
                     dtype=dtype,
                     mask=self.mask,
                     offset=self.offset,
                     null_count=self.null_count,
-                    children=children,
+                    children=self.children,
                 )
         else:
             raise ValueError("dtype must be IntervalDtype")

python/cudf/cudf/core/dtypes.py

@@ -257,7 +257,10 @@ def to_pandas(self) -> pd.CategoricalDtype:
     def _init_categories(self, categories: Any):
         if categories is None:
             return categories
-        if len(categories) == 0 and not _is_interval_dtype(categories):
+        if len(categories) == 0 and not isinstance(
+            getattr(categories, "dtype", None),
+            (cudf.IntervalDtype, pd.IntervalDtype),
+        ):
             dtype = "object"  # type: Any
         else:
             dtype = None