test: unxfail more modin tests (narwhals-dev#871)

narwhals/_pandas_like/series.py

@@ -515,13 +515,17 @@ def to_pandas(self) -> Any:
 
     # --- descriptive ---
     def is_duplicated(self: Self) -> Self:
-        return self._from_native_series(self._native_series.duplicated(keep=False))
+        res = self._native_series.duplicated(keep=False)
+        res = self._rename(res, self.name)
+        return self._from_native_series(res)
 
     def is_empty(self: Self) -> bool:
         return self._native_series.empty  # type: ignore[no-any-return]
 
     def is_unique(self: Self) -> Self:
-        return self._from_native_series(~self._native_series.duplicated(keep=False))
+        res = ~self._native_series.duplicated(keep=False)
+        res = self._rename(res, self.name)
+        return self._from_native_series(res)
 
     def null_count(self: Self) -> int:
         return self._native_series.isna().sum()  # type: ignore[no-any-return]

tests/expr_and_series/is_duplicated_test.py

@@ -1,7 +1,5 @@
 from typing import Any
 
-import pytest
-
 import narwhals.stable.v1 as nw
 from tests.utils import compare_dicts
 
@@ -11,10 +9,7 @@
 }
 
 
-def test_is_duplicated_expr(constructor: Any, request: Any) -> None:
-    if "modin" in str(constructor):
-        # TODO(unassigned): why is Modin failing here?
-        request.applymarker(pytest.mark.xfail)
+def test_is_duplicated_expr(constructor: Any) -> None:
     df = nw.from_native(constructor(data))
     result = df.select(nw.all().is_duplicated())
     expected = {

tests/expr_and_series/is_unique_test.py

@@ -1,7 +1,5 @@
 from typing import Any
 
-import pytest
-
 import narwhals.stable.v1 as nw
 from tests.utils import compare_dicts
 
@@ -11,10 +9,7 @@
 }
 
 
-def test_is_unique_expr(constructor: Any, request: Any) -> None:
-    if "modin" in str(constructor):
-        # TODO(unassigned): why is Modin failing here?
-        request.applymarker(pytest.mark.xfail)
+def test_is_unique_expr(constructor: Any) -> None:
     df = nw.from_native(constructor(data))
     result = df.select(nw.all().is_unique())
     expected = {