Remove aliases of various api.types APIs from utils.dtypes. (#9011)

Continuation of #8308 that moves all imports of standard dtype utilities to use `cudf.api.types` or `cudf.core.dtypes` rather than `cudf.util.dtypes`.

Authors:
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Ashwin Srinath (https://github.com/shwina)
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #9011

python/cudf/cudf/__init__.py

@@ -9,7 +9,7 @@
 import rmm
 
 from cudf.api.types import dtype
-from cudf import core, datasets, testing
+from cudf import api, core, datasets, testing
 from cudf._version import get_versions
 from cudf.api.extensions import (
     register_dataframe_accessor,

python/cudf/cudf/_lib/binaryop.pyx

@@ -24,7 +24,7 @@ from cudf._lib.cpp.scalar.scalar cimport scalar
 from cudf._lib.cpp.types cimport data_type, type_id
 from cudf._lib.types cimport dtype_to_data_type, underlying_type_t_type_id
 
-from cudf.utils.dtypes import is_scalar, is_string_dtype
+from cudf.api.types import is_scalar, is_string_dtype
 
 cimport cudf._lib.cpp.binaryop as cpp_binaryop
 from cudf._lib.cpp.binaryop cimport binary_operator

python/cudf/cudf/_lib/column.pyx

@@ -8,13 +8,13 @@ import rmm
 
 import cudf
 import cudf._lib as libcudfxx
-from cudf.core.buffer import Buffer
-from cudf.utils.dtypes import (
+from cudf.api.types import (
     is_categorical_dtype,
     is_decimal_dtype,
     is_list_dtype,
     is_struct_dtype,
 )
+from cudf.core.buffer import Buffer
 
 from cpython.buffer cimport PyObject_CheckBuffer
 from libc.stdint cimport uintptr_t

python/cudf/cudf/_lib/csv.pyx

@@ -262,7 +262,7 @@ cdef csv_reader_options make_csv_reader_options(
             csv_reader_options_c.set_dtypes(c_dtypes_map)
             csv_reader_options_c.set_parse_hex(c_hex_col_names)
         elif (
-            cudf.utils.dtypes.is_scalar(dtype) or
+            cudf.api.types.is_scalar(dtype) or
             isinstance(dtype, (
                 np.dtype, pd.core.dtypes.dtypes.ExtensionDtype, type
             ))
@@ -520,7 +520,7 @@ cdef data_type _get_cudf_data_type_from_dtype(object dtype) except +:
     # TODO: Remove this Error message once the
     # following issue is fixed:
     # https://github.com/rapidsai/cudf/issues/3960
-    if cudf.utils.dtypes.is_categorical_dtype(dtype):
+    if cudf.api.types.is_categorical_dtype(dtype):
         raise NotImplementedError(
             "CategoricalDtype as dtype is not yet "
             "supported in CSV reader"

python/cudf/cudf/_lib/groupby.pyx

@@ -7,7 +7,7 @@ from pandas.core.groupby.groupby import DataError
 
 import rmm
 
-from cudf.utils.dtypes import (
+from cudf.api.types import (
     is_categorical_dtype,
     is_decimal_dtype,
     is_interval_dtype,

python/cudf/cudf/_lib/io/utils.pyx

@@ -27,7 +27,7 @@ import io
 import os
 
 import cudf
-from cudf.utils.dtypes import is_struct_dtype
+from cudf.api.types import is_struct_dtype
 
 
 # Converts the Python source input to libcudf++ IO source_info

python/cudf/cudf/_lib/json.pyx

@@ -117,7 +117,7 @@ cpdef read_json(object filepaths_or_buffers,
                                 column_names=column_names)
 
 cdef data_type _get_cudf_data_type_from_dtype(object dtype) except +:
-    if cudf.utils.dtypes.is_categorical_dtype(dtype):
+    if cudf.api.types.is_categorical_dtype(dtype):
         raise NotImplementedError(
             "CategoricalDtype as dtype is not yet "
             "supported in JSON reader"

python/cudf/cudf/_lib/parquet.pyx

@@ -18,13 +18,13 @@ except ImportError:
 import numpy as np
 from cython.operator import dereference
 
-from cudf.utils.dtypes import (
+from cudf.api.types import (
     is_categorical_dtype,
     is_decimal_dtype,
     is_list_dtype,
     is_struct_dtype,
-    np_to_pa_dtype,
 )
+from cudf.utils.dtypes import np_to_pa_dtype
 
 from cudf._lib.utils cimport data_from_unique_ptr, get_column_names
 

python/cudf/cudf/_lib/reduce.pyx

@@ -1,7 +1,8 @@
 # Copyright (c) 2020-2021, NVIDIA CORPORATION.
 
 import cudf
-from cudf.utils.dtypes import is_decimal_dtype
+from cudf.api.types import is_decimal_dtype
+from cudf.core.dtypes import Decimal64Dtype
 
 from cudf._lib.column cimport Column
 from cudf._lib.cpp.column.column cimport column

python/cudf/cudf/_lib/replace.pyx

@@ -3,7 +3,7 @@
 from libcpp.memory cimport unique_ptr
 from libcpp.utility cimport move
 
-from cudf.utils.dtypes import is_scalar
+from cudf.api.types import is_scalar
 
 from cudf._lib.column cimport Column
 

python/cudf/cudf/_lib/scalar.pyx

@@ -65,8 +65,6 @@ from cudf._lib.cpp.wrappers.timestamps cimport (
 )
 from cudf._lib.utils cimport data_from_table_view
 
-import cudf
-
 
 cdef class DeviceScalar:
 

python/cudf/cudf/_lib/transpose.pyx

@@ -1,7 +1,7 @@
 # Copyright (c) 2020, NVIDIA CORPORATION.
 
 import cudf
-from cudf.utils.dtypes import is_categorical_dtype
+from cudf.api.types import is_categorical_dtype
 
 from libcpp.memory cimport unique_ptr
 from libcpp.pair cimport pair

python/cudf/cudf/_lib/unary.pyx

@@ -2,7 +2,7 @@
 
 from enum import IntEnum
 
-from cudf.utils.dtypes import is_decimal_dtype
+from cudf.api.types import is_decimal_dtype
 
 from libcpp cimport bool
 from libcpp.memory cimport unique_ptr

python/cudf/cudf/_lib/utils.pyx

@@ -22,14 +22,13 @@ try:
 except ImportError:
     import json
 
-from cudf.utils.dtypes import (
+from cudf.api.types import (
     is_categorical_dtype,
     is_decimal_dtype,
     is_list_dtype,
     is_struct_dtype,
-    np_dtypes_to_pandas_dtypes,
-    np_to_pa_dtype,
 )
+from cudf.utils.dtypes import np_dtypes_to_pandas_dtypes, np_to_pa_dtype
 
 PARQUET_META_TYPE_MAP = {
     str(cudf_dtype): str(pandas_dtype)

python/cudf/cudf/core/_base_index.py

@@ -10,15 +10,13 @@
 
 import cudf
 from cudf._typing import DtypeObj
-from cudf.api.types import is_dtype_equal, is_integer
+from cudf.api.types import is_dtype_equal, is_integer, is_list_like, is_scalar
 from cudf.core.abc import Serializable
 from cudf.core.column import ColumnBase, column
 from cudf.core.column_accessor import ColumnAccessor
 from cudf.utils import ioutils
 from cudf.utils.dtypes import (
-    is_list_like,
     is_mixed_with_object_dtype,
-    is_scalar,
     numeric_normalize_types,
 )
 from cudf.utils.utils import cached_property

python/cudf/cudf/core/_internals/where.py

@@ -41,10 +41,10 @@ def _check_and_cast_columns_with_other(
     Returns type-casted column `source_col` & scalar `other_scalar`
     based on `inplace` parameter.
     """
-    if cudf.utils.dtypes.is_categorical_dtype(source_col.dtype):
+    if cudf.api.types.is_categorical_dtype(source_col.dtype):
         return source_col, other
 
-    if cudf.utils.dtypes.is_scalar(other):
+    if cudf.api.types.is_scalar(other):
         device_obj = _normalize_scalars(source_col, other)
     else:
         device_obj = other
@@ -66,10 +66,8 @@ def _check_and_cast_columns_with_other(
         return source_col, device_obj.astype(source_col.dtype)
     else:
         if (
-            cudf.utils.dtypes.is_scalar(other)
-            and cudf.utils.dtypes._is_non_decimal_numeric_dtype(
-                source_col.dtype
-            )
+            cudf.api.types.is_scalar(other)
+            and cudf.api.types._is_non_decimal_numeric_dtype(source_col.dtype)
             and cudf.utils.dtypes._can_cast(other, source_col.dtype)
         ):
             common_dtype = source_col.dtype
@@ -82,11 +80,11 @@ def _check_and_cast_columns_with_other(
                 [
                     source_col.dtype,
                     np.min_scalar_type(other)
-                    if cudf.utils.dtypes.is_scalar(other)
+                    if cudf.api.types.is_scalar(other)
                     else other.dtype,
                 ]
             )
-            if cudf.utils.dtypes.is_scalar(device_obj):
+            if cudf.api.types.is_scalar(device_obj):
                 device_obj = cudf.Scalar(other, dtype=common_dtype)
             else:
                 device_obj = device_obj.astype(common_dtype)
@@ -132,17 +130,17 @@ def _normalize_columns_and_scalars_type(
             other_df._data[self_col] = other_col
         return source_df, other_df
 
-    elif isinstance(
-        frame, (Series, Index)
-    ) and not cudf.utils.dtypes.is_scalar(other):
+    elif isinstance(frame, (Series, Index)) and not cudf.api.types.is_scalar(
+        other
+    ):
         other = cudf.core.column.as_column(other)
         input_col = frame._data[frame.name]
         return _check_and_cast_columns_with_other(
             source_col=input_col, other=other, inplace=inplace
         )
     else:
         # Handles scalar or list/array like scalars
-        if isinstance(frame, (Series, Index)) and cudf.utils.dtypes.is_scalar(
+        if isinstance(frame, (Series, Index)) and cudf.api.types.is_scalar(
             other
         ):
             input_col = frame._data[frame.name]
@@ -162,7 +160,7 @@ def _normalize_columns_and_scalars_type(
                 ) = _check_and_cast_columns_with_other(
                     source_col=source_df._data[col_name],
                     other=other
-                    if cudf.utils.dtypes.is_scalar(other)
+                    if cudf.api.types.is_scalar(other)
                     else other[i],
                     inplace=inplace,
                 )
@@ -284,7 +282,7 @@ def where(
             other_column = others[i]
             if column_name in cond._data:
                 if isinstance(input_col, cudf.core.column.CategoricalColumn):
-                    if cudf.utils.dtypes.is_scalar(other_column):
+                    if cudf.api.types.is_scalar(other_column):
                         try:
                             other_column = input_col._encode(other_column)
                         except ValueError:
@@ -346,7 +344,7 @@ def where(
         )
 
         if isinstance(input_col, cudf.core.column.CategoricalColumn):
-            if cudf.utils.dtypes.is_scalar(other):
+            if cudf.api.types.is_scalar(other):
                 try:
                     other = input_col._encode(other)
                 except ValueError:

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

@@ -24,13 +24,12 @@
 from cudf import _lib as libcudf
 from cudf._lib.transform import bools_to_mask
 from cudf._typing import ColumnLike, Dtype, ScalarLike
+from cudf.api.types import is_categorical_dtype, is_interval_dtype
 from cudf.core.buffer import Buffer
 from cudf.core.column import column
 from cudf.core.column.methods import ColumnMethods
 from cudf.core.dtypes import CategoricalDtype
 from cudf.utils.dtypes import (
-    is_categorical_dtype,
-    is_interval_dtype,
     is_mixed_with_object_dtype,
     min_signed_type,
     min_unsigned_type,
@@ -802,7 +801,7 @@ def unary_operator(self, unaryop: str):
         )
 
     def __setitem__(self, key, value):
-        if cudf.utils.dtypes.is_scalar(
+        if cudf.api.types.is_scalar(
             value
         ) and cudf._lib.scalar._is_null_host_scalar(value):
             to_add_categories = 0
@@ -817,7 +816,7 @@ def __setitem__(self, key, value):
                 "category, set the categories first"
             )
 
-        if cudf.utils.dtypes.is_scalar(value):
+        if cudf.api.types.is_scalar(value):
             value = self._encode(value) if value is not None else value
         else:
             value = cudf.core.column.as_column(value).astype(self.dtype)

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

@@ -15,6 +15,7 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._typing import DatetimeLikeScalar, Dtype, DtypeObj, ScalarLike
+from cudf.api.types import is_scalar
 from cudf.core._compat import PANDAS_GE_120
 from cudf.core.buffer import Buffer
 from cudf.core.column import (
@@ -24,7 +25,6 @@
     column_empty_like,
     string,
 )
-from cudf.utils.dtypes import is_scalar
 from cudf.utils.utils import _fillna_natwise
 
 if PANDAS_GE_120:

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

@@ -15,11 +15,10 @@
     from_decimal as cpp_from_decimal,
 )
 from cudf._typing import Dtype
-from cudf.api.types import is_integer_dtype
+from cudf.api.types import is_integer_dtype, is_scalar
 from cudf.core.buffer import Buffer
 from cudf.core.column import ColumnBase, as_column
 from cudf.core.dtypes import Decimal32Dtype, Decimal64Dtype
-from cudf.utils.dtypes import is_scalar
 from cudf.utils.utils import pa_mask_buffer_to_mask
 
 from .numerical_base import NumericalBaseColumn

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

@@ -3,9 +3,9 @@
 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 IntervalDtype
-from cudf.utils.dtypes import is_interval_dtype
 
 
 class IntervalColumn(StructColumn):

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

@@ -19,11 +19,11 @@
 )
 from cudf._lib.table import Table
 from cudf._typing import BinaryOperand, ColumnLike, Dtype, ScalarLike
+from cudf.api.types import _is_non_decimal_numeric_dtype, is_list_dtype
 from cudf.core.buffer import Buffer
 from cudf.core.column import ColumnBase, as_column, column
 from cudf.core.column.methods import ColumnMethods, ParentType
 from cudf.core.dtypes import ListDtype
-from cudf.utils.dtypes import _is_non_decimal_numeric_dtype, is_list_dtype
 
 
 class ListColumn(ColumnBase):

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

@@ -128,7 +128,7 @@ def skew(self, skipna: bool = None) -> ScalarLike:
     def quantile(
         self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
     ) -> NumericalBaseColumn:
-        if isinstance(q, Number) or cudf.utils.dtypes.is_list_like(q):
+        if isinstance(q, Number) or cudf.api.types.is_list_like(q):
             np_array_q = np.asarray(q)
             if np.logical_or(np_array_q < 0, np_array_q > 1).any():
                 raise ValueError(

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

@@ -27,18 +27,18 @@
 from cudf import _lib as libcudf
 from cudf._lib import string_casting as str_cast, strings as libstrings
 from cudf._lib.column import Column
-from cudf.core.buffer import Buffer
-from cudf.core.column import column, datetime
-from cudf.core.column.methods import ColumnMethods, ParentType
-from cudf.utils import utils
-from cudf.utils.docutils import copy_docstring
-from cudf.utils.dtypes import (
-    can_convert_to_column,
+from cudf.api.types import (
     is_integer,
     is_list_dtype,
     is_scalar,
     is_string_dtype,
 )
+from cudf.core.buffer import Buffer
+from cudf.core.column import column, datetime
+from cudf.core.column.methods import ColumnMethods, ParentType
+from cudf.utils import utils
+from cudf.utils.docutils import copy_docstring
+from cudf.utils.dtypes import can_convert_to_column
 
 
 def str_to_boolean(column: StringColumn):

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

@@ -5,10 +5,10 @@
 
 import cudf
 from cudf._typing import Dtype
+from cudf.api.types import is_struct_dtype
 from cudf.core.column import ColumnBase, build_struct_column
 from cudf.core.column.methods import ColumnMethods
 from cudf.core.dtypes import StructDtype
-from cudf.utils.dtypes import is_struct_dtype
 
 
 class StructColumn(ColumnBase):