REF: Remove instances of pd.core

python/cudf/cudf/core/_compat.py

@@ -11,3 +11,4 @@
 PANDAS_LT_153 = PANDAS_VERSION < version.parse("1.5.3")
 PANDAS_GE_200 = PANDAS_VERSION >= version.parse("2.0.0")
 PANDAS_GE_210 = PANDAS_VERSION >= version.parse("2.1.0")
+PANDAS_GE_220 = PANDAS_VERSION >= version.parse("2.2.0")

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

@@ -27,13 +27,17 @@
     is_scalar,
     is_timedelta64_dtype,
 )
+from cudf.core._compat import PANDAS_GE_220
 from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
 from cudf.core.column import ColumnBase, as_column, column, string
 from cudf.core.column.timedelta import _unit_to_nanoseconds_conversion
 from cudf.utils.dtypes import _get_base_dtype
 from cudf.utils.utils import _all_bools_with_nulls
 
-_guess_datetime_format = pd.core.tools.datetimes.guess_datetime_format
+if PANDAS_GE_220:
+    _guess_datetime_format = pd.tseries.api.guess_datetime_format
+else:
+    _guess_datetime_format = pd.core.tools.datetimes.guess_datetime_format
 
 # nanoseconds per time_unit
 _dtype_to_format_conversion = {

python/cudf/cudf/core/dataframe.py

@@ -2313,8 +2313,19 @@ def to_dict(
         if orient == "series":
             # Special case needed to avoid converting
             # cudf.Series objects into pd.Series
-            into_c = pd.core.common.standardize_mapping(into)
-            return into_c((k, v) for k, v in self.items())
+            if not inspect.isclass(into):
+                cons = type(into)  # type: ignore[assignment]
+                if isinstance(into, defaultdict):
+                    cons = functools.partial(cons, into.default_factory)
+            elif issubclass(into, abc.Mapping):
+                cons = into  # type: ignore[assignment]
+                if issubclass(into, defaultdict):
+                    raise TypeError(
+                        "to_dict() only accepts initialized defaultdicts"
+                    )
+            else:
+                raise TypeError(f"unsupported type: {into}")
+            return cons(self.items())  # type: ignore[misc]
 
         return self.to_pandas().to_dict(orient=orient, into=into)
 

python/cudf/cudf/core/index.py

@@ -330,17 +330,15 @@ def _data(self):
 
     @_cudf_nvtx_annotate
     def __contains__(self, item):
-        if not isinstance(
+        if isinstance(item, bool) or not isinstance(
             item, tuple(np.sctypes["int"] + np.sctypes["float"] + [int, float])
         ):
             return False
         try:
-            item = pd.core.dtypes.common.ensure_python_int(item)
-        except TypeError:
-            return False
-        if not item % 1 == 0:
+            int_item = int(item)
+            return int_item == item and int_item in self._range
+        except (ValueError, OverflowError):
             return False
-        return item in range(self._start, self._stop, self._step)
 
     @_cudf_nvtx_annotate
     def copy(self, name=None, deep=False, dtype=None, names=None):

python/cudf/cudf/tests/test_rolling.py

@@ -8,7 +8,7 @@
 import pytest
 
 import cudf
-from cudf.core._compat import PANDAS_GE_150, PANDAS_LT_140
+from cudf.core._compat import PANDAS_GE_150
 from cudf.testing._utils import (
     _create_pandas_series_float64_default,
     assert_eq,
@@ -536,10 +536,9 @@ def get_window_bounds(
     "indexer",
     [
         pd.api.indexers.FixedForwardWindowIndexer(window_size=2),
-        pd.core.window.expanding.ExpandingIndexer(),
-        pd.core.window.indexers.FixedWindowIndexer(window_size=3)
-        if PANDAS_LT_140
-        else pd.core.indexers.objects.FixedWindowIndexer(window_size=3),
+        pd.api.indexers.VariableOffsetWindowIndexer(
+            index=pd.date_range("2020", periods=5), offset=pd.offsets.BDay(1)
+        ),
     ],
 )
 def test_rolling_indexer_support(indexer):