update(violin-basic): plotnine — comprehensive quality review (#4327)

## Summary

Updated **plotnine** 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/plotnine.py

@@ -1,49 +1,75 @@
 """ pyplots.ai
 violin-basic: Basic Violin Plot
-Library: plotnine 0.15.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: plotnine 0.15.3 | Python 3.14.3
+Quality: 92/100 | Updated: 2026-02-21
 """
 
 import numpy as np
 import pandas as pd
-from plotnine import aes, element_text, geom_violin, ggplot, labs, scale_fill_manual, theme, theme_minimal
+from plotnine import (
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_violin,
+    ggplot,
+    labs,
+    scale_fill_manual,
+    theme,
+    theme_minimal,
+)
 
 
 # Data
 np.random.seed(42)
 
-categories = ["Group A", "Group B", "Group C", "Group D"]
-data = []
+records = []
+
+# Biology: right-skewed (many average, few high scorers)
+scores_bio = np.concatenate([np.random.normal(68, 6, 150), np.random.normal(85, 3, 30)])
+records.extend([("Biology", s) for s in scores_bio])
+
+# Statistics: bimodal (two distinct clusters)
+scores_stat = np.concatenate([np.random.normal(55, 5, 100), np.random.normal(82, 5, 100)])
+records.extend([("Statistics", s) for s in scores_stat])
 
-# Generate data with different distributions for each category
-distributions = {
-    "Group A": (50, 10),  # mean, std
-    "Group B": (65, 15),
-    "Group C": (45, 8),
-    "Group D": (70, 12),
-}
+# Chemistry: tight normal (consistent performance)
+scores_chem = np.random.normal(74, 4, 200)
+records.extend([("Chemistry", s) for s in scores_chem])
 
-for category, (mean, std) in distributions.items():
-    n_points = 200
-    values = np.random.normal(mean, std, n_points)
-    data.extend([(category, v) for v in values])
+# Psychology: wide spread (high variance)
+scores_psych = np.random.normal(70, 14, 200)
+records.extend([("Psychology", s) for s in scores_psych])
+
+df = pd.DataFrame(records, columns=["course", "score"])
+df["score"] = df["score"].clip(0, 100)
+df["course"] = pd.Categorical(
+    df["course"], categories=["Biology", "Statistics", "Chemistry", "Psychology"], ordered=True
+)
 
-df = pd.DataFrame(data, columns=["category", "value"])
+# Custom palette: Python Blue for focal bimodal distribution, muted tones for context
+palette = {"Biology": "#7BAE7F", "Statistics": "#306998", "Chemistry": "#E8A87C", "Psychology": "#B8B3D6"}
 
 # Plot
 plot = (
-    ggplot(df, aes(x="category", y="value", fill="category"))
-    + geom_violin(draw_quantiles=[0.25, 0.5, 0.75], size=1)
-    + scale_fill_manual(values=["#306998", "#FFD43B", "#4B8BBE", "#FFE873"])
-    + labs(x="Category", y="Value", title="violin-basic · plotnine · pyplots.ai")
+    ggplot(df, aes(x="course", y="score", fill="course"))
+    + geom_violin(draw_quantiles=[0.25, 0.5, 0.75], alpha=0.82, color="#666666", size=0.35, trim=False)
+    + scale_fill_manual(values=palette)
+    + labs(x="Course", y="Final Exam Score (pts)", title="violin-basic \u00b7 plotnine \u00b7 pyplots.ai")
     + theme_minimal()
     + theme(
         figure_size=(16, 9),
-        text=element_text(size=14),
-        axis_title=element_text(size=20),
-        axis_text=element_text(size=16),
-        plot_title=element_text(size=24),
+        text=element_text(size=14, color="#333333"),
+        axis_title=element_text(size=20, color="#333333"),
+        axis_text=element_text(size=16, color="#444444"),
+        plot_title=element_text(size=24, color="#222222", weight="bold"),
         legend_position="none",
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_grid_major_y=element_line(color="#dedede", size=0.3),
+        plot_background=element_rect(fill="#fafafa", color="none"),
+        panel_background=element_rect(fill="#fafafa", color="none"),
     )
 )