Make cudf.api.types imports consistent (#8571)

We usually don't use relative imports in our code-base, this PR makes the import statements consistent.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Charles Blackmon-Luca (https://github.com/charlesbluca)

URL: #8571

python/cudf/cudf/core/dataframe.py

@@ -25,6 +25,7 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._lib.null_mask import MaskState, create_null_mask
+from cudf.api.types import is_bool_dtype, is_dict_like
 from cudf.core import column, reshape
 from cudf.core.abc import Serializable
 from cudf.core.column import as_column, column_empty
@@ -54,8 +55,6 @@
 )
 from cudf.utils.utils import GetAttrGetItemMixin
 
-from ..api.types import is_bool_dtype, is_dict_like
-
 T = TypeVar("T", bound="DataFrame")
 
 

python/cudf/cudf/core/frame.py

@@ -17,6 +17,7 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._typing import ColumnLike, DataFrameOrSeries
+from cudf.api.types import is_dict_like, is_dtype_equal
 from cudf.core.column import (
     ColumnBase,
     as_column,
@@ -36,8 +37,6 @@
     min_scalar_type,
 )
 
-from ..api.types import is_dict_like, is_dtype_equal
-
 T = TypeVar("T", bound="Frame")
 
 if TYPE_CHECKING:

python/cudf/cudf/core/index.py

@@ -17,6 +17,12 @@
 from cudf._lib.search import search_sorted
 from cudf._lib.table import Table
 from cudf._typing import DtypeObj
+from cudf.api.types import (
+    _is_scalar_or_zero_d_array,
+    is_dtype_equal,
+    is_integer,
+    is_string_dtype,
+)
 from cudf.core.abc import Serializable
 from cudf.core.column import (
     CategoricalColumn,
@@ -47,13 +53,6 @@
 )
 from cudf.utils.utils import cached_property, search_range
 
-from ..api.types import (
-    _is_scalar_or_zero_d_array,
-    is_dtype_equal,
-    is_integer,
-    is_string_dtype,
-)
-
 
 class BaseIndex(SingleColumnFrame, Serializable):
     """Base class for all cudf Index types."""

python/cudf/cudf/core/indexing.py

@@ -11,6 +11,12 @@
 from cudf._lib.concat import concat_columns
 from cudf._lib.scalar import _is_null_host_scalar
 from cudf._typing import ColumnLike, DataFrameOrSeries, ScalarLike
+from cudf.api.types import (
+    is_bool_dtype,
+    is_integer,
+    is_integer_dtype,
+    is_numeric_dtype,
+)
 from cudf.core.column.column import as_column
 from cudf.utils.dtypes import (
     _is_non_decimal_numeric_dtype,
@@ -23,13 +29,6 @@
     to_cudf_compatible_scalar,
 )
 
-from ..api.types import (
-    is_bool_dtype,
-    is_integer,
-    is_integer_dtype,
-    is_numeric_dtype,
-)
-
 
 def indices_from_labels(obj, labels):
     from cudf.core.column import column

python/cudf/cudf/core/series.py

@@ -18,6 +18,7 @@
 import cudf
 from cudf import _lib as libcudf
 from cudf._lib.transform import bools_to_mask
+from cudf.api.types import is_bool_dtype, is_dict_like, is_dtype_equal
 from cudf.core.abc import Serializable
 from cudf.core.column import (
     DatetimeColumn,
@@ -61,8 +62,6 @@
     get_relevant_submodule,
 )
 
-from ..api.types import is_bool_dtype, is_dict_like, is_dtype_equal
-
 
 class Series(SingleColumnFrame, Serializable):
     # The `constructor*` properties are used by `dask` (and `dask_cudf`)

python/cudf/cudf/utils/dtypes.py

@@ -11,9 +11,7 @@
 from pandas.core.dtypes.common import infer_dtype_from_object
 
 import cudf
-from cudf.core._compat import PANDAS_GE_120
-
-from ..api.types import (  # noqa: F401
+from cudf.api.types import (  # noqa: F401
     _is_non_decimal_numeric_dtype,
     _is_scalar_or_zero_d_array,
     is_categorical_dtype,
@@ -30,6 +28,7 @@
     is_struct_dtype,
     is_timedelta_dtype,
 )
+from cudf.core._compat import PANDAS_GE_120
 
 _NA_REP = "<NA>"
 _np_pa_dtypes = {