Replace _is_categorical_dtype with isinstance

python/cudf/cudf/_fuzz_testing/csv.py

@@ -99,7 +99,7 @@ def set_rand_params(self, params):
                     if dtype_val is not None:
                         dtype_val = {
                             col_name: "category"
-                            if cudf.utils.dtypes._is_categorical_dtype(dtype)
+                            if isinstance(dtype, cudf.CategoricalDtype)
                             else pandas_dtypes_to_np_dtypes[dtype]
                             for col_name, dtype in dtype_val.items()
                         }

python/cudf/cudf/_fuzz_testing/json.py

@@ -27,7 +27,7 @@ def _get_dtype_param_value(dtype_val):
     if dtype_val is not None and isinstance(dtype_val, abc.Mapping):
         processed_dtypes = {}
         for col_name, dtype in dtype_val.items():
-            if cudf.utils.dtypes._is_categorical_dtype(dtype):
+            if isinstance(dtype, cudf.CategoricalDtype):
                 processed_dtypes[col_name] = "category"
             else:
                 processed_dtypes[col_name] = str(

python/cudf/cudf/_lib/csv.pyx

@@ -434,19 +434,19 @@ def read_csv(
     if dtype is not None:
         if isinstance(dtype, abc.Mapping):
             for k, v in dtype.items():
-                if cudf.api.types._is_categorical_dtype(v):
+                if isinstance(cudf.dtype(v), cudf.CategoricalDtype):
                     df._data[str(k)] = df._data[str(k)].astype(v)
         elif (
             cudf.api.types.is_scalar(dtype) or
             isinstance(dtype, (
                 np.dtype, pd.api.extensions.ExtensionDtype, type
             ))
         ):
-            if cudf.api.types._is_categorical_dtype(dtype):
+            if isinstance(cudf.dtype(v), cudf.CategoricalDtype):
                 df = df.astype(dtype)
         elif isinstance(dtype, abc.Collection):
             for index, col_dtype in enumerate(dtype):
-                if cudf.api.types._is_categorical_dtype(col_dtype):
+                if isinstance(cudf.dtype(v), cudf.CategoricalDtype):
                     col_name = df._data.names[index]
                     df._data[col_name] = df._data[col_name].astype(col_dtype)
 
@@ -554,7 +554,7 @@ cdef data_type _get_cudf_data_type_from_dtype(object dtype) except *:
     # TODO: Remove this work-around Dictionary types
     # in libcudf are fully mapped to categorical columns:
     # https://github.com/rapidsai/cudf/issues/3960
-    if cudf.api.types._is_categorical_dtype(dtype):
+    if isinstance(cudf.dtype(dtype), cudf.CategoricalDtype):
         if isinstance(dtype, str):
             dtype = "str"
         else:

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

@@ -52,7 +52,6 @@
 from cudf._lib.types import size_type_dtype
 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,
@@ -968,7 +967,8 @@ def astype(self, dtype: Dtype, copy: bool = False) -> ColumnBase:
             col = self
         if self.dtype == dtype:
             return col
-        if _is_categorical_dtype(dtype):
+        dtype = cudf.dtype(dtype)
+        if isinstance(dtype, cudf.CategoricalDtype):
             return col.as_categorical_column(dtype)
 
         if (
@@ -987,7 +987,7 @@ def astype(self, dtype: Dtype, copy: bool = False) -> ColumnBase:
             dtype = pandas_dtypes_to_np_dtypes.get(dtype, dtype)
         if _is_non_decimal_numeric_dtype(dtype):
             return col.as_numerical_column(dtype)
-        elif _is_categorical_dtype(dtype):
+        elif isinstance(dtype, cudf.CategoricalDtype):
             return col.as_categorical_column(dtype)
         elif cudf.dtype(dtype).type in {
             np.str_,
@@ -1396,7 +1396,7 @@ def column_empty_like(
 
     if (
         hasattr(column, "dtype")
-        and _is_categorical_dtype(column.dtype)
+        and isinstance(column.dtype, cudf.CategoricalDtype)
         and dtype == column.dtype
     ):
         catcolumn = cast("cudf.core.column.CategoricalColumn", column)
@@ -2263,7 +2263,10 @@ def as_column(
         np_type = None
         try:
             if dtype is not None:
-                if _is_categorical_dtype(dtype) or _is_interval_dtype(dtype):
+                dtype = cudf.dtype(dtype)
+                if isinstance(
+                    dtype, (cudf.CategoricalDtype, cudf.IntervalDtype)
+                ):
                     raise TypeError
                 if _is_datetime64tz_dtype(dtype):
                     raise NotImplementedError(
@@ -2407,7 +2410,7 @@ def as_column(
         except (pa.ArrowInvalid, pa.ArrowTypeError, TypeError) as e:
             if isinstance(e, MixedTypeError):
                 raise TypeError(str(e))
-            if _is_categorical_dtype(dtype):
+            if isinstance(cudf.dtype(dtype), cudf.CategoricalDtype):
                 sr = pd.Series(arbitrary, dtype="category")
                 data = as_column(sr, nan_as_null=nan_as_null, dtype=dtype)
             elif np_type == np.str_:

python/cudf/cudf/core/dtypes.py

@@ -1008,7 +1008,7 @@ def _is_categorical_dtype(obj):
             pd.Series,
         ),
     ):
-        return _is_categorical_dtype(obj.dtype)
+        return isinstance(cudf.dtype(obj.dtype), cudf.CategoricalDtype)
     if hasattr(obj, "type"):
         if obj.type is pd_CategoricalDtypeType:
             return True

python/cudf/cudf/testing/testing.py

@@ -8,11 +8,7 @@
 
 import cudf
 from cudf._lib.unary import is_nan
-from cudf.api.types import (
-    _is_categorical_dtype,
-    is_numeric_dtype,
-    is_string_dtype,
-)
+from cudf.api.types import is_numeric_dtype, is_string_dtype
 from cudf.core.missing import NA, NaT
 
 
@@ -86,7 +82,7 @@ def _check_types(
     if (
         exact
         and not isinstance(left, cudf.MultiIndex)
-        and _is_categorical_dtype(left)
+        and isinstance(left.dtype, cudf.CategoricalDtype)
     ):
         if left.dtype != right.dtype:
             raise_assert_detail(
@@ -144,8 +140,8 @@ def assert_column_equal(
     """
     if check_dtype is True:
         if (
-            _is_categorical_dtype(left)
-            and _is_categorical_dtype(right)
+            isinstance(left.dtype, cudf.CategoricalDtype)
+            and isinstance(right.dtype, cudf.CategoricalDtype)
             and not check_categorical
         ):
             pass
@@ -173,7 +169,9 @@ def assert_column_equal(
             return
 
     if check_exact and check_categorical:
-        if _is_categorical_dtype(left) and _is_categorical_dtype(right):
+        if isinstance(left.dtype, cudf.CategoricalDtype) and isinstance(
+            right.dtype, cudf.CategoricalDtype
+        ):
             left_cat = left.categories
             right_cat = right.categories
 
@@ -207,8 +205,8 @@ def assert_column_equal(
 
     if (
         not check_dtype
-        and _is_categorical_dtype(left)
-        and _is_categorical_dtype(right)
+        and isinstance(left.dtype, cudf.CategoricalDtype)
+        and isinstance(right.dtype, cudf.CategoricalDtype)
     ):
         left = left.astype(left.categories.dtype)
         right = right.astype(right.categories.dtype)
@@ -258,7 +256,9 @@ def assert_column_equal(
                 raise e
             else:
                 columns_equal = False
-            if _is_categorical_dtype(left) and _is_categorical_dtype(right):
+            if isinstance(left.dtype, cudf.CategoricalDtype) and isinstance(
+                right.dtype, cudf.CategoricalDtype
+            ):
                 left = left.astype(left.categories.dtype)
                 right = right.astype(right.categories.dtype)
     if not columns_equal:

python/cudf/cudf/tests/test_column.py

@@ -81,7 +81,7 @@ def test_column_offset_and_size(pandas_input, offset, size):
         children=col.base_children,
     )
 
-    if cudf.api.types._is_categorical_dtype(col.dtype):
+    if isinstance(col.dtype, cudf.CategoricalDtype):
         assert col.size == col.codes.size
         assert col.size == (col.codes.data.size / col.codes.dtype.itemsize)
     elif cudf.api.types.is_string_dtype(col.dtype):
@@ -120,7 +120,7 @@ def column_slicing_test(col, offset, size, cast_to_float=False):
     else:
         pd_series = series.to_pandas()
 
-    if cudf.api.types._is_categorical_dtype(col.dtype):
+    if isinstance(col.dtype, cudf.CategoricalDtype):
         # The cudf.Series is constructed from an already sliced column, whereas
         # the pandas.Series is constructed from the unsliced series and then
         # sliced, so the indexes should be different and we must ignore it.

python/cudf/cudf/tests/test_concat.py

@@ -9,7 +9,6 @@
 import pytest
 
 import cudf as gd
-from cudf.api.types import _is_categorical_dtype
 from cudf.core._compat import PANDAS_GE_200
 from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype, Decimal128Dtype
 from cudf.testing._utils import (
@@ -600,8 +599,8 @@ def test_concat_empty_dataframes(df, other, ignore_index):
     actual = gd.concat(other_gd, ignore_index=ignore_index)
     if expected.shape != df.shape:
         for key, col in actual[actual.columns].items():
-            if _is_categorical_dtype(col.dtype):
-                if not _is_categorical_dtype(expected[key].dtype):
+            if isinstance(col.dtype, gd.CategoricalDtype):
+                if not isinstance(expected[key].dtype, gd.CategoricalDtype):
                     # TODO: Pandas bug:
                     # https://github.com/pandas-dev/pandas/issues/42840
                     expected[key] = expected[key].fillna("-1").astype("str")
@@ -1203,8 +1202,10 @@ def test_concat_join_empty_dataframes(
     if expected.shape != df.shape:
         if axis == 0:
             for key, col in actual[actual.columns].items():
-                if _is_categorical_dtype(col.dtype):
-                    if not _is_categorical_dtype(expected[key].dtype):
+                if isinstance(col.dtype, gd.CategoricalDtype):
+                    if not isinstance(
+                        expected[key].dtype, gd.CategoricalDtype
+                    ):
                         # TODO: Pandas bug:
                         # https://github.com/pandas-dev/pandas/issues/42840
                         expected[key] = (
@@ -1323,7 +1324,7 @@ def test_concat_join_empty_dataframes_axis_1(
     if expected.shape != df.shape:
         if axis == 0:
             for key, col in actual[actual.columns].items():
-                if _is_categorical_dtype(col.dtype):
+                if isinstance(col.dtype, gd.CategoricalDtype):
                     expected[key] = expected[key].fillna("-1")
                     actual[key] = col.astype("str").fillna("-1")
             # if not expected.empty:

python/cudf/cudf/utils/dtypes.py

@@ -171,8 +171,8 @@ def cudf_dtype_from_pydata_dtype(dtype):
     """Given a numpy or pandas dtype, converts it into the equivalent cuDF
     Python dtype.
     """
-
-    if cudf.api.types._is_categorical_dtype(dtype):
+    dtype = cudf.dtype(dtype)
+    if isinstance(dtype, cudf.CategoricalDtype):
         return cudf.core.dtypes.CategoricalDtype
     elif cudf.api.types.is_decimal32_dtype(dtype):
         return cudf.core.dtypes.Decimal32Dtype
@@ -416,9 +416,9 @@ def get_min_float_dtype(col):
 
 
 def is_mixed_with_object_dtype(lhs, rhs):
-    if cudf.api.types._is_categorical_dtype(lhs.dtype):
+    if isinstance(lhs.dtype, cudf.CategoricalDtype):
         return is_mixed_with_object_dtype(lhs.dtype.categories, rhs)
-    elif cudf.api.types._is_categorical_dtype(rhs.dtype):
+    elif isinstance(rhs.dtype, cudf.CategoricalDtype):
         return is_mixed_with_object_dtype(lhs, rhs.dtype.categories)
 
     return (lhs.dtype == "object" and rhs.dtype != "object") or (
@@ -518,10 +518,10 @@ def find_common_type(dtypes):
 
     # Early exit for categoricals since they're not hashable and therefore
     # can't be put in a set.
-    if any(cudf.api.types._is_categorical_dtype(dtype) for dtype in dtypes):
+    if any(isinstance(dtype, cudf.CategoricalDtype) for dtype in dtypes):
         if all(
             (
-                cudf.api.types._is_categorical_dtype(dtype)
+                isinstance(dtype, cudf.CategoricalDtype)
                 and (not dtype.ordered if hasattr(dtype, "ordered") else True)
             )
             for dtype in dtypes

python/dask_cudf/dask_cudf/backends.py

@@ -333,7 +333,7 @@ def percentile_cudf(a, q, interpolation="linear"):
     if isinstance(q, Iterator):
         q = list(q)
 
-    if cudf.api.types._is_categorical_dtype(a.dtype):
+    if isinstance(a.dtype, cudf.CategoricalDtype):
         result = cp.percentile(a.cat.codes, q, interpolation=interpolation)
 
         return (

python/dask_cudf/dask_cudf/sorting.py

@@ -17,7 +17,6 @@
 from dask.utils import M
 
 import cudf as gd
-from cudf.api.types import _is_categorical_dtype
 from cudf.utils.nvtx_annotation import _dask_cudf_nvtx_annotate
 
 _SHUFFLE_SUPPORT = ("tasks", "p2p")  # "disk" not supported
@@ -230,7 +229,7 @@ def quantile_divisions(df, by, npartitions):
     if (
         len(columns) == 1
         and df[columns[0]].dtype != "object"
-        and not _is_categorical_dtype(df[columns[0]].dtype)
+        and not isinstance(df[columns[0]].dtype, gd.CategoricalDtype)
     ):
         dtype = df[columns[0]].dtype
         divisions = divisions[columns[0]].astype("int64")