separate booleans

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

@@ -1948,19 +1948,25 @@ def as_column(
                 raise TypeError(
                     f"Cannot convert a {inferred_dtype} of object type"
                 )
+            elif inferred_dtype == "boolean":
+                if cudf.get_option("mode.pandas_compatible"):
+                    if (
+                        dtype is not None
+                        and dtype == np.dtype("bool")
+                        and not pd.isna(arbitrary).any()
+                    ):
+                        pass
+                    else:
+                        raise MixedTypeError(
+                            f"Cannot have mixed values with {inferred_dtype}"
+                        )
+                elif nan_as_null is False and _has_any_nan(arbitrary):
+                    raise MixedTypeError(
+                        f"Cannot have mixed values with {inferred_dtype}"
+                    )
             elif (
-                cudf.get_option("mode.pandas_compatible")
-                and inferred_dtype == "boolean"
-            ):
-                raise MixedTypeError(
-                    f"Cannot have mixed values with {inferred_dtype}"
-                )
-            elif nan_as_null is False and (
-                any(
-                    (isinstance(x, (np.floating, float)) and np.isnan(x))
-                    or (inferred_dtype == "boolean" and pd.isna(arbitrary))
-                    for x in np.asarray(arbitrary)
-                )
+                nan_as_null is False
+                and _has_any_nan(arbitrary)
                 and inferred_dtype not in ("decimal", "empty")
             ):
                 # Decimal can hold float("nan")
@@ -2338,3 +2344,10 @@ def concat_columns(objs: "MutableSequence[ColumnBase]") -> ColumnBase:
 
     # Filter out inputs that have 0 length, then concatenate.
     return libcudf.concat.concat_columns([o for o in objs if len(o)])
+
+
+def _has_any_nan(arbitrary):
+    return any(
+        (isinstance(x, (np.floating, float)) and np.isnan(x))
+        for x in np.asarray(arbitrary)
+    )

python/cudf/cudf/tests/test_series.py

@@ -2358,13 +2358,23 @@ def test_multi_dim_series_error():
 
 def test_bool_series_mixed_dtype_error():
     ps = pd.Series([True, False, None])
+    all_bool_ps = pd.Series([True, False, True], dtype="object")
     # ps now has `object` dtype, which
     # isn't supported by `cudf`.
     with cudf.option_context("mode.pandas_compatible", True):
         with pytest.raises(TypeError):
             cudf.Series(ps)
         with pytest.raises(TypeError):
             cudf.from_pandas(ps)
+        with pytest.raises(TypeError):
+            cudf.Series(ps, dtype=bool)
+        expected = cudf.Series(all_bool_ps, dtype=bool)
+        assert_eq(expected, all_bool_ps.astype(bool))
+    nan_bools_mix = pd.Series([True, False, True, np.nan], dtype="object")
+    gs = cudf.Series(nan_bools_mix, nan_as_null=True)
+    assert_eq(gs.to_pandas(nullable=True), nan_bools_mix.astype("boolean"))
+    with pytest.raises(TypeError):
+        cudf.Series(nan_bools_mix, nan_as_null=False)
 
 
 @pytest.mark.parametrize(