update(violin-basic): letsplot — comprehensive quality review (#4330)

## Summary

Updated **letsplot** implementation for **violin-basic**.

**Changes:** Comprehensive quality review improving code quality, data
choice, visual design, spec compliance, and library feature usage.

### Changes
- Improved data generation with distinct distribution shapes per
category
- Enhanced visual design (explicit font sizes, refined color palette,
layout balance)
- Fixed review weaknesses from previous evaluation
- Updated metadata with current library/Python versions
- Preview images uploaded to GCS staging

## 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/violin-basic/implementations/letsplot.py

@@ -1,7 +1,7 @@
 """ pyplots.ai
 violin-basic: Basic Violin Plot
-Library: letsplot 4.8.1 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: letsplot 4.8.2 | Python 3.14.3
+Quality: 90/100 | Updated: 2026-02-21
 """
 
 import numpy as np
@@ -15,53 +15,66 @@
 # Data
 np.random.seed(42)
 
-categories = ["Engineering", "Marketing", "Sales", "Design"]
-colors = ["#306998", "#FFD43B", "#4B8BBE", "#FFE873"]
+# Ordered by median salary (high → low) for visual storytelling
+dept_order = ["Engineering", "Design", "Marketing", "Sales"]
+palette = ["#306998", "#2E8B57", "#E8A317", "#E07A5F"]
 
-# Generate realistic salary distributions per department
 data = []
-distributions = {
-    "Engineering": {"mean": 95000, "std": 20000, "n": 200},
-    "Marketing": {"mean": 75000, "std": 15000, "n": 150},
-    "Sales": {"mean": 70000, "std": 25000, "n": 180},
-    "Design": {"mean": 80000, "std": 18000, "n": 120},
-}
 
-for cat in categories:
-    dist = distributions[cat]
-    values = np.random.normal(dist["mean"], dist["std"], dist["n"])
-    values = np.clip(values, 30000, 200000)  # Realistic salary bounds
-    for v in values:
-        data.append({"Department": cat, "Salary": v})
+# Engineering: bimodal (junior ~$70k + senior ~$115k) — showcases violin strength
+eng_junior = np.random.normal(70000, 8000, 80)
+eng_senior = np.random.normal(115000, 12000, 120)
+eng_values = np.clip(np.concatenate([eng_junior, eng_senior]), 30000, 200000)
+for v in eng_values:
+    data.append({"Department": "Engineering", "Salary": v})
+
+# Design: moderate spread, roughly normal
+design_values = np.random.normal(80000, 18000, 120)
+design_values = np.clip(design_values, 30000, 200000)
+for v in design_values:
+    data.append({"Department": "Design", "Salary": v})
+
+# Marketing: narrower with a small cluster of high earners
+mkt_base = np.random.normal(72000, 12000, 130)
+mkt_high = np.random.normal(105000, 8000, 20)
+mkt_values = np.clip(np.concatenate([mkt_base, mkt_high]), 30000, 200000)
+for v in mkt_values:
+    data.append({"Department": "Marketing", "Salary": v})
+
+# Sales: right-skewed (many moderate earners, few top performers)
+sales_values = np.random.exponential(20000, 180) + 45000
+sales_values = np.clip(sales_values, 30000, 200000)
+for v in sales_values:
+    data.append({"Department": "Sales", "Salary": v})
 
 df = pd.DataFrame(data)
 
 # Plot
 plot = (
     ggplot(df, aes(x="Department", y="Salary", fill="Department"))  # noqa: F405
     + geom_violin(  # noqa: F405
-        quantiles=[0.25, 0.5, 0.75],  # Show quartiles including median
-        quantile_lines=True,  # Draw lines at quantiles
-        size=1.5,  # Border thickness
-        alpha=0.8,
-        trim=False,  # Show full tails
+        quantiles=[0.25, 0.5, 0.75], quantile_lines=True, size=1.2, alpha=0.85, trim=False, color="#2C3E50"
+    )
+    + scale_x_discrete(limits=dept_order)  # noqa: F405
+    + scale_fill_manual(values=dict(zip(dept_order, palette, strict=True)))  # noqa: F405
+    + scale_y_continuous(  # noqa: F405
+        format="${,.0f}"
     )
-    + scale_fill_manual(values=colors)  # noqa: F405
     + labs(  # noqa: F405
-        x="Department", y="Salary ($)", title="violin-basic · lets-plot · pyplots.ai"
+        x="Department", y="Salary", title="violin-basic \u00b7 letsplot \u00b7 pyplots.ai"
     )
     + theme_minimal()  # noqa: F405
     + theme(  # noqa: F405
         axis_title=element_text(size=20),  # noqa: F405
         axis_text=element_text(size=16),  # noqa: F405
         plot_title=element_text(size=24),  # noqa: F405
-        legend_position="none",  # Legend not needed, x-axis shows categories
+        legend_position="none",
+        panel_grid_major_x=element_blank(),  # noqa: F405
+        axis_ticks=element_blank(),  # noqa: F405
     )
     + ggsize(1600, 900)  # noqa: F405
 )
 
-# Save PNG (scale 3x to get 4800 × 2700 px)
+# Save
 export_ggsave(plot, filename="plot.png", path=".", scale=3)
-
-# Save HTML for interactive version
 export_ggsave(plot, filename="plot.html", path=".")