Fix boolean casting consistency with Pandas (#20746)

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

@@ -490,7 +490,7 @@ def as_string_column(self, dtype: DtypeObj) -> StringColumn:
             )
         if len(self) == 0:
             return cast(
-                cudf.core.column.StringColumn,
+                "cudf.core.column.StringColumn",
                 column_empty(0, dtype=CUDF_STRING_DTYPE),
             )
 
@@ -576,6 +576,20 @@ def as_numerical_column(self, dtype: DtypeObj) -> NumericalColumn:
                 res = self.nans_to_nulls().cast(dtype=dtype)
                 res._dtype = dtype
                 return res  # type: ignore[return-value]
+
+            # --- FIX: Match Pandas behavior when casting Float(with Nulls) -> Bool ---
+            # Pandas treats NaN as truthy (True) when casting float -> bool.
+            # In cuDF, Nulls propagate. We must fill Nulls with np.nan so the
+            # cast treats them as True.
+            if (
+                self.dtype.kind == "f"
+                and dtype.kind == "b"
+                and not is_pandas_nullable_extension_dtype(dtype)
+                and self.has_nulls()
+            ):
+                return self.fillna(np.nan).cast(dtype=dtype)  # type: ignore[return-value]
+            # ------------------------------------------------------------------------
+
             if dtype_to_pylibcudf_type(dtype) == dtype_to_pylibcudf_type(
                 self.dtype
             ):
@@ -746,7 +760,7 @@ def find_and_replace(
             replacement_col = replacement_col.repeat(len(to_replace_col))
         elif len(replacement_col) == 1 and len(to_replace_col) == 0:
             return self.copy()
-        replaced = cast(Self, self.astype(common_type))
+        replaced = cast("Self", self.astype(common_type))
         df = cudf.DataFrame._from_data(
             {
                 "old": to_replace_col.astype(common_type),
@@ -910,7 +924,7 @@ def _with_type_metadata(
     ) -> ColumnBase:
         if isinstance(dtype, CategoricalDtype):
             codes_dtype = min_unsigned_type(len(dtype.categories))
-            codes = cast(NumericalColumn, self.astype(codes_dtype))
+            codes = cast("NumericalColumn", self.astype(codes_dtype))
             return CategoricalColumn(
                 plc_column=codes.to_pylibcudf(mode="read"),
                 size=codes.size,

python/cudf/cudf/tests/test_issue_20746.py

@@ -0,0 +1,44 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pandas as pd
+
+import cudf
+from cudf.testing import assert_series_equal
+
+
+def test_cast_float_nan_to_bool_pandas_compat():
+    """
+    Regression test for Issue #20746.
+    Ensures that casting float columns with NaNs to boolean
+    treats NaNs as True (matching Pandas behavior) when
+    mode.pandas_compatible is enabled.
+    """
+    # Enable pandas compatibility mode
+    cudf.set_option("mode.pandas_compatible", True)
+
+    try:
+        data = [1.0, 0.0, np.nan, None]
+
+        # Create cuDF Series
+        gs = cudf.Series(data, dtype="float64")
+
+        # Cast to bool
+        got = gs.astype("bool")
+
+        # Create expected Pandas Series (Pandas casts NaN/None to True)
+        expected = pd.Series([True, False, True, True], dtype="bool")
+
+        # Verify
+        # In Pandas compat mode, we expect NO nulls in the boolean result
+        assert got.null_count == 0
+
+        # Convert to pandas for easy comparison or use testing utils
+        expected_cudf = cudf.Series(expected)
+
+        assert_series_equal(got, expected_cudf)
+
+    finally:
+        # Reset option to avoid side effects on other tests
+        cudf.set_option("mode.pandas_compatible", False)