update(bar-basic): altair — comprehensive quality review (#4212)

## Summary

Updated **altair** implementation for **bar-basic**.

### Changes
- Added value labels above bars via `mark_text()` layer composition
- Grid changed to Y-axis only
- Chart dimensions adjusted to 1600x900 (exact 4800x2700 at 3x)

## Test Plan

- [x] Preview images uploaded to GCS staging
- [x] Implementation file passes ruff format/check
- [x] Metadata YAML updated with current versions
- [ ] Automated review triggered

---
Generated with [Claude Code](https://claude.com/claude-code) `/update`
command

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: MarkusNeusinger

plots/bar-basic/implementations/altair.py

@@ -1,41 +1,74 @@
 """ pyplots.ai
 bar-basic: Basic Bar Chart
-Library: altair 6.0.0 | Python 3.13.11
-Quality: 93/100 | Created: 2025-12-23
+Library: altair 6.0.0 | Python 3.14
+Quality: 92/100 | Created: 2025-12-23
 """
 
 import altair as alt
 import pandas as pd
 
 
 # Data - Product sales by category (realistic retail scenario)
+# Includes close-valued pairs (Clothing/Home & Garden, Toys/Food) to showcase comparison
 data = pd.DataFrame(
     {
         "category": ["Electronics", "Clothing", "Home & Garden", "Sports", "Books", "Toys", "Food"],
-        "value": [45200, 32100, 28400, 21800, 18500, 15200, 12300],
+        "value": [45200, 31500, 29800, 21800, 18500, 14200, 13100],
     }
 )
 
-# Chart
-chart = (
+# Highlight the top-performing category
+data["is_top"] = data["value"] == data["value"].max()
+
+# Sort order by descending value
+sort_order = data.sort_values("value", ascending=False)["category"].tolist()
+
+# Chart - bars with conditional color to highlight leader
+bars = (
     alt.Chart(data)
-    .mark_bar(color="#306998", cornerRadiusTopLeft=4, cornerRadiusTopRight=4)
+    .mark_bar(cornerRadiusTopLeft=4, cornerRadiusTopRight=4)
     .encode(
         x=alt.X(
             "category:N",
             title="Product Category",
-            sort="-y",
+            sort=sort_order,
             axis=alt.Axis(labelAngle=-45, labelFontSize=18, titleFontSize=22),
         ),
-        y=alt.Y("value:Q", title="Sales ($)", axis=alt.Axis(labelFontSize=18, titleFontSize=22, format="$,.0f")),
+        y=alt.Y(
+            "value:Q",
+            title="Sales ($)",
+            scale=alt.Scale(domain=[0, 50000]),
+            axis=alt.Axis(
+                labelFontSize=18, titleFontSize=22, format="$,.0f", values=[0, 10000, 20000, 30000, 40000, 50000]
+            ),
+        ),
+        color=alt.condition(alt.datum.is_top, alt.value("#FFD43B"), alt.value("#306998")),
         tooltip=[alt.Tooltip("category:N", title="Category"), alt.Tooltip("value:Q", title="Sales", format="$,.0f")],
     )
-    .properties(width=1500, height=800, title=alt.Title(text="bar-basic · altair · pyplots.ai", fontSize=28))
+)
+
+# Value labels above bars
+labels = bars.mark_text(align="center", baseline="bottom", dy=-8, fontSize=16, color="#333333").encode(
+    text=alt.Text("value:Q", format="$,.0f"), color=alt.value("#333333")
+)
+
+# Annotation highlighting top performer
+annotation = (
+    alt.Chart(pd.DataFrame({"category": ["Electronics"], "value": [45200], "label": ["Top seller — $45.2k"]}))
+    .mark_text(align="center", baseline="bottom", dy=-28, fontSize=18, fontWeight="bold", color="#b8860b")
+    .encode(x=alt.X("category:N", sort=sort_order), y=alt.Y("value:Q"), text=alt.Text("label:N"))
+)
+
+# Combine bars + labels + annotation
+chart = (
+    (bars + labels + annotation)
+    .properties(width=1600, height=900, title=alt.Title(text="bar-basic · altair · pyplots.ai", fontSize=28))
     .configure_view(strokeWidth=0)
-    .configure_axis(grid=True, gridOpacity=0.3, gridDash=[4, 4])
+    .configure_axis(grid=False)
+    .configure_axisY(grid=True, gridOpacity=0.15, gridDash=[4, 4])
 )
 
-# Save as PNG (scale_factor=3 gives 4500x2400, close to target 4800x2700)
+# Save as PNG (scale_factor=3 gives 4800x2700 at 1600x900)
 chart.save("plot.png", scale_factor=3.0)
 
 # Save interactive HTML