Remove cudf compat gating at more places

python/cudf/cudf/core/accessors/string.py

@@ -24,7 +24,6 @@
 from cudf.core.accessors.lists import ListMethods
 from cudf.core.column.column import ColumnBase, as_column, column_empty
 from cudf.core.dtypes import ListDtype
-from cudf.options import get_option
 from cudf.utils.dtypes import (
     CUDF_STRING_DTYPE,
     can_convert_to_column,
@@ -4203,10 +4202,8 @@ def index(
 
         if (result == -1).any():
             raise ValueError("substring not found")
-        elif get_option("mode.pandas_compatible"):
-            return result.astype(np.dtype(np.int64))
         else:
-            return result
+            return result.astype(np.dtype(np.int64))
 
     def rindex(
         self, sub: str, start: int = 0, end: int | None = None
@@ -4265,10 +4262,8 @@ def rindex(
 
         if (result == -1).any():
             raise ValueError("substring not found")
-        elif get_option("mode.pandas_compatible"):
-            return result.astype(np.dtype(np.int64))
         else:
-            return result
+            return result.astype(np.dtype(np.int64))
 
     def match(
         self,

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

@@ -1661,13 +1661,7 @@ def notnull(self) -> ColumnBase:
                     plc.unary.is_valid(self.plc_column), np.dtype(np.bool_)
                 )
 
-        if cudf.get_option("mode.pandas_compatible"):
-            return result
-
-        return ColumnBase.create(
-            result.plc_column,
-            get_dtype_of_same_kind(self.dtype, np.dtype(np.bool_)),
-        )
+        return result
 
     @cached_property
     def nan_count(self) -> int:
@@ -3027,8 +3021,7 @@ def as_column(
                     )
                 )
             elif (
-                cudf.get_option("mode.pandas_compatible")
-                and isinstance(arbitrary.dtype, pd.CategoricalDtype)
+                isinstance(arbitrary.dtype, pd.CategoricalDtype)
                 and is_pandas_nullable_extension_dtype(
                     arbitrary.dtype.categories.dtype
                 )

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

@@ -11,7 +11,6 @@
 
 import pylibcudf as plc
 
-import cudf
 from cudf.core.column.column import ColumnBase, _handle_nulls, as_column
 from cudf.core.dtype.validators import is_dtype_obj_interval
 from cudf.core.dtypes import IntervalDtype, _dtype_to_metadata
@@ -267,8 +266,6 @@ def element_indexing(
         if isinstance(result, pa.Scalar):
             py_element = maybe_nested_pa_scalar_to_py(result)
             result = self.dtype._recursively_replace_fields(py_element)  # type: ignore[union-attr]
-        if isinstance(result, dict) and cudf.get_option(
-            "mode.pandas_compatible"
-        ):
+        if isinstance(result, dict):
             return pd.Interval(**result, closed=self.dtype.closed)  # type: ignore[union-attr]
         return result

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

@@ -232,12 +232,7 @@ def notnull(self) -> ColumnBase:
                 # For floats, NaN should be considered null
                 result = result & self.notnan()
 
-        if cudf.get_option("mode.pandas_compatible"):
-            return result
-
-        return result._with_type_metadata(
-            get_dtype_of_same_kind(self.dtype, np.dtype(np.bool_))
-        )
+        return result
 
     def element_indexing(self, index: int) -> ScalarLike | None:
         result = super().element_indexing(index)

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

@@ -229,11 +229,7 @@ def element_indexing(self, index: int) -> ScalarLike:
         if result is self._PANDAS_NA_VALUE:
             return result
         result = result.as_py()
-        if cudf.get_option("mode.pandas_compatible"):
-            return self._PD_SCALAR(result)
-        elif isinstance(result, self._PD_SCALAR):
-            return result.to_numpy()
-        return self.dtype.type(result).astype(self.dtype, copy=False)
+        return self._PD_SCALAR(result)
 
     def to_pandas(
         self,
@@ -310,11 +306,11 @@ def can_cast_safely(self, to_dtype: DtypeObj) -> bool:
             to_res, _ = np.datetime_data(to_dtype)
             max_val = self.max()
             if isinstance(max_val, (pd.Timedelta, pd.Timestamp)):
-                max_val = max_val.to_numpy()
+                max_val = max_val.to_numpy().astype(self.dtype)
             max_val = max_val.astype(self._UNDERLYING_DTYPE, copy=False)
             min_val = self.min()
             if isinstance(min_val, (pd.Timedelta, pd.Timestamp)):
-                min_val = min_val.to_numpy()
+                min_val = min_val.to_numpy().astype(self.dtype)
             min_val = min_val.astype(self._UNDERLYING_DTYPE, copy=False)
             # call-overload must be ignored because numpy stubs only accept literal strings
             # for time units (e.g., "ns", "us") to allow compile-time validation,

python/cudf/cudf/core/dtypes.py

@@ -1065,14 +1065,12 @@ def to_arrow(self) -> ArrowIntervalType:
         )
 
     def to_pandas(self) -> pd.IntervalDtype:
-        if cudf.get_option("mode.pandas_compatible"):
-            return pd.IntervalDtype(
-                subtype=self.subtype.numpy_dtype
-                if is_pandas_nullable_extension_dtype(self.subtype)
-                else self.subtype,
-                closed=self.closed,
-            )
-        return pd.IntervalDtype(subtype=self.subtype, closed=self.closed)
+        return pd.IntervalDtype(
+            subtype=self.subtype.numpy_dtype
+            if is_pandas_nullable_extension_dtype(self.subtype)
+            else self.subtype,
+            closed=self.closed,
+        )
 
     def __eq__(self, other) -> bool:
         if isinstance(other, str):

python/cudf/cudf/core/frame.py

@@ -725,9 +725,7 @@ def to_array(
                     elif to_dtype.kind in "ui":
                         to_dtype = np.dtype("float64")
 
-            if cudf.get_option(
-                "mode.pandas_compatible"
-            ) and is_pandas_nullable_extension_dtype(to_dtype):
+            if is_pandas_nullable_extension_dtype(to_dtype):
                 to_dtype = getattr(to_dtype, "numpy_dtype", to_dtype)
                 if getattr(to_dtype, "kind", None) == "U":
                     to_dtype = np.dtype(object)

python/cudf/cudf/core/indexed_frame.py

@@ -519,12 +519,9 @@ def _scan(
             if cast_to_int and result_col.dtype.kind in "uib":
                 # For reductions that accumulate a value (e.g. sum, not max)
                 # pandas returns an int64 dtype for all int or bool dtypes.
-                if cudf.get_option("mode.pandas_compatible"):
-                    dtype = get_dtype_of_same_kind(
-                        result_col.dtype, np.dtype(np.int64)
-                    )
-                else:
-                    dtype = np.dtype(np.int64)
+                dtype = get_dtype_of_same_kind(
+                    result_col.dtype, np.dtype(np.int64)
+                )
                 result_col = result_col.astype(dtype)
             results.append(getattr(result_col, op)(inclusive=True))
         return self._from_data_like_self(
@@ -7045,27 +7042,29 @@ def _append_new_row_inplace(col: ColumnBase, value: ScalarLike) -> None:
         val_col = val_col.astype(col.dtype)
         to_type = col.dtype
     else:
-        if (
-            cudf.get_option("mode.pandas_compatible")
-            and is_pandas_nullable_extension_dtype(col.dtype)
-            and val_col.dtype.kind == "f"
-        ):
-            # If the column is a pandas nullable extension type, we need to
-            # convert the nans to a nullable type as well.
-            val_col = val_col.nans_to_nulls()
-            if len(val_col) == val_col.null_count:
-                # If the column is all nulls, we can use the column dtype
-                # to avoid unnecessary casting.
-                val_col = val_col.astype(col.dtype)
-        to_type = find_common_type([val_col.dtype, col.dtype])
+        to_type = None
+        if is_pandas_nullable_extension_dtype(col.dtype):
+            if val_col.dtype.kind == "f":
+                # If the column is a pandas nullable extension type, we need to
+                # convert the nans to a nullable type as well.
+                val_col = val_col.nans_to_nulls()
+                if len(val_col) == val_col.null_count:
+                    # If the column is all nulls, we can use the column dtype
+                    # to avoid unnecessary casting.
+                    val_col = val_col.astype(col.dtype)
+            if val_col.can_cast_safely(col.dtype):
+                to_type = col.dtype
+        if to_type is None:
+            to_type = find_common_type([val_col.dtype, col.dtype])
+
         if (
             cudf.get_option("mode.pandas_compatible")
             and is_string_dtype(to_type)
             and is_mixed_with_object_dtype(val_col, col)
         ):
             raise MixedTypeError("Cannot append mixed types")
-        if cudf.get_option(
-            "mode.pandas_compatible"
+        if is_pandas_nullable_extension_dtype(
+            col.dtype
         ) and val_col.can_cast_safely(col.dtype):
             to_type = col.dtype
     val_col = val_col.astype(to_type)

python/cudf/cudf/core/series.py

@@ -1918,10 +1918,8 @@ def any(
                 "The bool_only parameter is not supported for Series."
             )
         result = super().any(axis, skipna, **kwargs)
-        if (
-            cudf.get_option("mode.pandas_compatible")
-            and isinstance(result, bool)
-            and not isinstance(self.dtype, pd.ArrowDtype)
+        if isinstance(result, bool) and not isinstance(
+            self.dtype, pd.ArrowDtype
         ):
             return np.bool_(result)
         return result

python/cudf/cudf/pandas/scripts/conftest-patch.py

@@ -4256,7 +4256,6 @@ def set_copy_on_write_option():
     "tests/indexes/multi/test_sorting.py::test_remove_unused_levels_large[datetime64[D]-str]",
     "tests/indexes/multi/test_sorting.py::test_remove_unused_levels_large[int64-int64]",
     "tests/indexes/multi/test_sorting.py::test_unsortedindex_doc_examples",
-    "tests/indexes/numeric/test_astype.py::TestAstype::test_astype_float64_to_uint64",
     "tests/indexes/numeric/test_indexing.py::TestGetIndexer::test_get_indexer_invalid",
     "tests/indexes/numeric/test_indexing.py::TestGetIndexer::test_get_indexer_nan",
     "tests/indexes/numeric/test_indexing.py::TestGetIndexer::test_get_indexer_nearest_decreasing[backfill-expected1]",
@@ -4865,7 +4864,6 @@ def set_copy_on_write_option():
     "tests/indexing/test_partial.py::TestEmptyFrameSetitemExpansion::test_partial_set_empty_frame3",
     "tests/indexing/test_partial.py::TestEmptyFrameSetitemExpansion::test_partial_set_empty_frame5",
     "tests/indexing/test_partial.py::TestPartialSetting::test_loc_with_list_of_strings_representing_datetimes[DataFrame-idx1-labels1-expected_idx1]",
-    "tests/indexing/test_partial.py::TestPartialSetting::test_partial_setting",
     "tests/indexing/test_partial.py::TestPartialSetting::test_partial_setting_frame",
     "tests/indexing/test_partial.py::TestPartialSetting::test_partial_setting_mixed_dtype",
     "tests/indexing/test_scalar.py::TestMultiIndexScalar::test_multiindex_at_get_one_level",

python/cudf/cudf/tests/input_output/test_csv.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2018-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2018-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 import codecs
@@ -1077,7 +1077,7 @@ def test_csv_reader_tabs():
     np.testing.assert_allclose(floats, df["float_point"].to_numpy())
     np.testing.assert_allclose(ints, df["integer"].to_numpy())
     for row in range(4):
-        assert str(df["date"][row]) == dates[row]
+        assert str(df["date"][row].to_numpy()) == dates[row]
 
 
 @pytest.mark.parametrize("segment_bytes", [10000, 19999, 30001, 36000])