chore: use more robust dtype comparisons, use Narwhals stable API in …

…tests (vega#3670)

altair/utils/core.py

@@ -709,11 +709,14 @@ def infer_vegalite_type_for_narwhals(
         and not (categories := column.cat.get_categories()).is_empty()
     ):
         return "ordinal", categories.to_list()
-    if dtype in {nw.String, nw.Categorical, nw.Boolean}:
+    if dtype == nw.String or dtype == nw.Categorical or dtype == nw.Boolean:  # noqa: PLR1714
         return "nominal"
     elif dtype.is_numeric():
         return "quantitative"
-    elif dtype in {nw.Datetime, nw.Date}:
+    elif dtype == nw.Datetime or dtype == nw.Date:  # noqa: PLR1714
+        # We use `== nw.Datetime` to check for any kind of Datetime, regardless of time
+        # unit and time zone. Prefer this over `dtype in {nw.Datetime, nw.Date}`,
+        # see https://narwhals-dev.github.io/narwhals/backcompat.
         return "temporal"
     else:
         msg = f"Unexpected DtypeKind: {dtype}"

tests/test_transformed_data.py

@@ -7,7 +7,7 @@
 import altair as alt
 from altair.utils.execeval import eval_block
 from tests import examples_methods_syntax, slow, ignore_DataFrameGroupBy
-import narwhals as nw
+import narwhals.stable.v1 as nw
 
 try:
     import vegafusion as vf