Scope DataFrameLike claims to tested backends

Remove cuDF/Modin name-drops from the docstring, error message and
tests. They are reachable through Narwhals like any other backend but
are not exercised here (no GPU; Modin's pinned deps conflict), so
naming them as features overclaims. The validation path is described
generically as "any Narwhals-supported backend" with pandas, Polars
and PyArrow as the tested set. Drops the permanently-skipped Modin
test.

Author: ghostiee-11

param/parameters.py

@@ -3527,13 +3527,11 @@ class DataFrameLike(ClassSelector[t.Any]):
     Unlike :class:`DataFrame`, which is restricted to ``pandas.DataFrame``,
     ``DataFrameLike`` accepts any object supported by
     `Narwhals <https://narwhals-dev.github.io>`_. pandas, Polars and PyArrow
-    are exercised in this project's test suite; Modin and cuDF are supported
-    through the same Narwhals code path but are not run in CI (Modin's pinned
-    dependencies conflict with the test environment and cuDF is GPU-only).
-    The value is passed through unchanged, so reading the parameter returns
-    the original native object (no Narwhals wrapper). Authors who want a
-    backend-agnostic API can call ``narwhals.from_native`` on the value
-    themselves.
+    are exercised in this project's test suite; any other Narwhals-supported
+    backend uses the identical code path. The value is passed through
+    unchanged, so reading the parameter returns the original native object
+    (no Narwhals wrapper). Authors who want a backend-agnostic API can call
+    ``narwhals.from_native`` on the value themselves.
 
     Narwhals is an optional dependency, imported on instantiation; a clear
     ``ImportError`` with the install command is raised if it is missing. The
@@ -3652,8 +3650,8 @@ def _as_narwhals(self, val):
             kind = 'a dataframe-like' if self.allow_lazy else 'an eager dataframe-like'
             raise ValueError(
                 f"{_validate_error_prefix(self)} value must be {kind} object "
-                f"that Narwhals recognises (pandas, Polars, PyArrow, cuDF, "
-                f"Modin), not {type(val).__name__!r}."
+                f"that Narwhals recognises (pandas, Polars, PyArrow, ...), "
+                f"not {type(val).__name__!r}."
             ) from e
 
     def _validate(self, val):

tests/testdataframelike.py

@@ -30,17 +30,9 @@
 except ModuleNotFoundError:
     pa = None
 
-try:
-    import modin.pandas as mpd
-except Exception:
-    # modin import can fail for reasons beyond ModuleNotFoundError (missing
-    # execution engine), so guard broadly and skip rather than error.
-    mpd = None
-
 skip_no_pandas = pytest.mark.skipif(pd is None, reason="pandas not available")
 skip_no_polars = pytest.mark.skipif(pl is None, reason="polars not available")
 skip_no_pyarrow = pytest.mark.skipif(pa is None, reason="pyarrow not available")
-skip_no_modin = pytest.mark.skipif(mpd is None, reason="modin not available")
 
 
 class TestDataFrameLikeDefaults(unittest.TestCase):
@@ -85,18 +77,6 @@ class P(param.Parameterized):
         src = pa.table({'a': [1, 2]})
         self.assertIs(P(df=src).df, src)
 
-    @skip_no_modin
-    def test_modin(self):
-        class P(param.Parameterized):
-            df = param.DataFrameLike(
-                default=pd.DataFrame({'a': [1]}), rows=2, columns={'a'})
-        src = mpd.DataFrame({'a': [1, 2]})
-        p = P(df=src)
-        self.assertIs(p.df, src)
-        self.assertIsInstance(p.df, mpd.DataFrame)
-        with self.assertRaises(ValueError):
-            P(df=mpd.DataFrame({'a': [1]}))  # rows=2 mismatch
-
 
 @skip_no_pandas
 class TestDataFrameLikeRejects(unittest.TestCase):