Allow None when nan_as_null=False in column constructor

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

@@ -1948,8 +1948,19 @@ def as_column(
                 raise TypeError(
                     f"Cannot convert a {inferred_dtype} of object type"
                 )
+            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 (
-                pd.isna(arbitrary).any()
+                any(
+                    (isinstance(x, (np.floating, float)) and np.isnan(x))
+                    or (inferred_dtype == "boolean" and pd.isna(arbitrary))
+                    for x in np.asarray(arbitrary)
+                )
                 and inferred_dtype not in ("decimal", "empty")
             ):
                 # Decimal can hold float("nan")

python/cudf/cudf/tests/test_dataframe.py

@@ -4008,44 +4008,28 @@ def test_diff(dtype, period, data_empty):
 
 @pytest.mark.parametrize("df", _dataframe_na_data())
 @pytest.mark.parametrize("nan_as_null", [True, False, None])
-def test_dataframe_isnull_isna(df, nan_as_null):
-    if nan_as_null is False and (
-        df.select_dtypes(object).isna().any().any()
-        and not df.select_dtypes(object).isna().all().all()
-    ):
-        with pytest.raises(MixedTypeError):
-            cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
-    else:
-        gdf = cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
+@pytest.mark.parametrize("api_call", ["isnull", "isna", "notna", "notnull"])
+def test_dataframe_isnull_isna_and_reverse(df, nan_as_null, api_call):
+    def detect_nan(x):
+        # Check if the input is a float and if it is nan
+        return x.apply(lambda v: isinstance(v, float) and np.isnan(v))
 
-        assert_eq(df.isnull(), gdf.isnull())
-        assert_eq(df.isna(), gdf.isna())
-
-        # Test individual columns
-        for col in df:
-            assert_eq(df[col].isnull(), gdf[col].isnull())
-            assert_eq(df[col].isna(), gdf[col].isna())
-
-
-@pytest.mark.parametrize("df", _dataframe_na_data())
-@pytest.mark.parametrize("nan_as_null", [True, False, None])
-def test_dataframe_notna_notnull(df, nan_as_null):
+    nan_contains = df.select_dtypes(object).apply(detect_nan)
     if nan_as_null is False and (
-        df.select_dtypes(object).isna().any().any()
-        and not df.select_dtypes(object).isna().all().all()
+        nan_contains.any().any() and not nan_contains.all().all()
     ):
         with pytest.raises(MixedTypeError):
             cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
     else:
         gdf = cudf.DataFrame.from_pandas(df, nan_as_null=nan_as_null)
 
-        assert_eq(df.notnull(), gdf.notnull())
-        assert_eq(df.notna(), gdf.notna())
+        assert_eq(getattr(df, api_call)(), getattr(gdf, api_call)())
 
         # Test individual columns
         for col in df:
-            assert_eq(df[col].notnull(), gdf[col].notnull())
-            assert_eq(df[col].notna(), gdf[col].notna())
+            assert_eq(
+                getattr(df[col], api_call)(), getattr(gdf[col], api_call)()
+            )
 
 
 def test_ndim():

python/cudf/cudf/tests/test_series.py

@@ -774,8 +774,9 @@ def test_round_nan_as_null_false(series, decimal):
 @pytest.mark.parametrize("ps", _series_na_data())
 @pytest.mark.parametrize("nan_as_null", [True, False, None])
 def test_series_isnull_isna(ps, nan_as_null):
+    nan_contains = ps.apply(lambda x: isinstance(x, float) and np.isnan(x))
     if nan_as_null is False and (
-        ps.isna().any() and not ps.isna().all() and ps.dtype == object
+        nan_contains.any() and not nan_contains.all() and ps.dtype == object
     ):
         with pytest.raises(MixedTypeError):
             cudf.Series.from_pandas(ps, nan_as_null=nan_as_null)
@@ -789,8 +790,9 @@ def test_series_isnull_isna(ps, nan_as_null):
 @pytest.mark.parametrize("ps", _series_na_data())
 @pytest.mark.parametrize("nan_as_null", [True, False, None])
 def test_series_notnull_notna(ps, nan_as_null):
+    nan_contains = ps.apply(lambda x: isinstance(x, float) and np.isnan(x))
     if nan_as_null is False and (
-        ps.isna().any() and not ps.isna().all() and ps.dtype == object
+        nan_contains.any() and not nan_contains.all() and ps.dtype == object
     ):
         with pytest.raises(MixedTypeError):
             cudf.Series.from_pandas(ps, nan_as_null=nan_as_null)
@@ -2358,10 +2360,11 @@ def test_bool_series_mixed_dtype_error():
     ps = pd.Series([True, False, None])
     # ps now has `object` dtype, which
     # isn't supported by `cudf`.
-    with pytest.raises(TypeError):
-        cudf.Series(ps, nan_as_null=False)
-    with pytest.raises(TypeError):
-        cudf.from_pandas(ps, nan_as_null=False)
+    with cudf.option_context("mode.pandas_compatible", True):
+        with pytest.raises(TypeError):
+            cudf.Series(ps)
+        with pytest.raises(TypeError):
+            cudf.from_pandas(ps)
 
 
 @pytest.mark.parametrize(