fix(python): satisfy ruff and ty after rebase

Use datetime.UTC, annotate _time_zone_scan, type the datetime unit
mapping for _dtype.timestamp, and build expected frames with
pl.DataFrame so ty can see a DataFrame rather than DataFrame | Series.

Signed-off-by: Claude <noreply@anthropic.com>
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01E92wUoS3LaqWVJmmS7vKVq

Author: claude

vortex-python/python/vortex/polars_.py

@@ -4,7 +4,7 @@
 import json
 import operator
 from collections.abc import Callable
-from typing import Any, cast
+from typing import Any, Literal, cast
 
 import polars as pl
 
@@ -199,7 +199,12 @@ def _polars_to_vortex(expr: dict[str, Any]) -> ve.Expr:
 
 def _datetime_literal(data: list[Any]) -> ve.Expr:
     value, unit, tz = data
-    units = {"Nanoseconds": "ns", "Microseconds": "us", "Milliseconds": "ms", "Seconds": "s"}
+    units: dict[str, Literal["s", "ms", "us", "ns"]] = {
+        "Nanoseconds": "ns",
+        "Microseconds": "us",
+        "Milliseconds": "ms",
+        "Seconds": "s",
+    }
     if unit not in units:
         raise NotImplementedError(f"Unsupported Polars date time unit: {unit}")
     if isinstance(tz, dict):

vortex-python/test/test_polars_.py

@@ -3,9 +3,8 @@
 
 import math
 import os
-from datetime import date, datetime, time, timezone
+from datetime import UTC, date, datetime, time
 from decimal import Decimal
-from datetime import datetime, timezone
 from zoneinfo import ZoneInfo
 
 import polars as pl
@@ -213,15 +212,15 @@ def test_datetime_predicate_pushdown(tmp_path):
         {
             "id": [0, 1, 2],
             "value": pa.array(
-                [datetime(2026, 9, day, tzinfo=timezone.utc) for day in [16, 17, 18]],
+                [datetime(2026, 9, day, tzinfo=UTC) for day in [16, 17, 18]],
                 type=pa.timestamp("us", tz="UTC"),
             ),
         }
     )
     path = tmp_path / "datetimes.vortex"
     vx.io.write(vx.array(table), str(path))
-    predicate = pl.col("value") >= datetime(2026, 9, 17, tzinfo=timezone.utc)
-    expected = pl.from_arrow(table).lazy().filter(predicate).collect()
+    predicate = pl.col("value") >= datetime(2026, 9, 17, tzinfo=UTC)
+    expected = pl.DataFrame(table).lazy().filter(predicate).collect()
     result = vx.open(str(path)).to_polars().filter(predicate).collect()
     assert_frame_equal(result, expected)
     assert result["id"].to_list() == [1, 2]
@@ -283,9 +282,7 @@ def test_replace_time_zone_policy_column(tmp_path, fold, expected):
 
 
 def test_replace_time_zone_non_existent_null(tmp_path):
-    values = pa.array(
-        [datetime(2024, 3, 10, 2, 30), datetime(2024, 3, 10, 3, 30)], type=pa.timestamp("us")
-    )
+    values = pa.array([datetime(2024, 3, 10, 2, 30), datetime(2024, 3, 10, 3, 30)], type=pa.timestamp("us"))
     reference, frame = _time_zone_scan(tmp_path, values)
     expression = pl.col("dt").dt.replace_time_zone("America/New_York", non_existent="null")
     threshold = datetime(2024, 3, 10, 3, 30, tzinfo=ZoneInfo("America/New_York"))
@@ -307,9 +304,7 @@ def test_replace_time_zone_raises(tmp_path, value):
 
 
 def test_replace_time_zone_maps_to_native_expression():
-    expression = pl.col("dt").dt.replace_time_zone(
-        "America/New_York", ambiguous=pl.col("policy"), non_existent="null"
-    )
+    expression = pl.col("dt").dt.replace_time_zone("America/New_York", ambiguous=pl.col("policy"), non_existent="null")
     expected = ve.replace_time_zone(
         ve.column("dt"), "America/New_York", ambiguous=ve.column("policy"), non_existent="null"
     )
@@ -328,11 +323,11 @@ def test_replace_time_zone_same_zone_during_fold(tmp_path):
     assert actual["id"].to_list() == [0]
 
 
-def _time_zone_scan(tmp_path, values, policy=None):
+def _time_zone_scan(tmp_path, values, policy=None) -> tuple[pl.LazyFrame, pl.LazyFrame]:
     columns = {"id": pa.array(range(len(values))), "dt": values}
     if policy is not None:
         columns["policy"] = pa.array(policy)
     path = tmp_path / "timezones.vortex"
     table = pa.table(columns)
     vx.io.write(vx.array(table), str(path))
-    return pl.from_arrow(table).lazy(), vx.open(str(path)).to_polars()
+    return pl.DataFrame(table).lazy(), vx.open(str(path)).to_polars()