feat(plotnine): implement violin-box (#2684)

## Implementation: `violin-box` - plotnine

Implements the **plotnine** version of `violin-box`.

**File:** `plots/violin-box/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595340834)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/violin-box/implementations/plotnine.py

@@ -0,0 +1,61 @@
+""" pyplots.ai
+violin-box: Violin Plot with Embedded Box Plot
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_text, geom_boxplot, geom_violin, ggplot, labs, scale_fill_manual, theme, theme_minimal
+
+
+# Data
+np.random.seed(42)
+n_per_group = 80
+
+# Create groups with different distributions
+data = pd.DataFrame(
+    {
+        "Value": np.concatenate(
+            [
+                np.random.normal(55, 10, n_per_group),  # Group A: normal
+                np.random.exponential(8, n_per_group) + 35,  # Group B: right-skewed
+                np.concatenate(
+                    [  # Group C: bimodal
+                        np.random.normal(40, 5, n_per_group // 2),
+                        np.random.normal(65, 5, n_per_group // 2),
+                    ]
+                ),
+                np.random.uniform(30, 70, n_per_group),  # Group D: uniform
+            ]
+        ),
+        "Group": ["Product A"] * n_per_group
+        + ["Product B"] * n_per_group
+        + ["Product C"] * n_per_group
+        + ["Product D"] * n_per_group,
+    }
+)
+
+# Colors: Python Blue and Yellow plus complementary colors
+colors = ["#306998", "#FFD43B", "#6A9BC9", "#D4A84B"]
+
+# Plot
+plot = (
+    ggplot(data, aes(x="Group", y="Value", fill="Group"))
+    + geom_violin(alpha=0.7, color="#333333", size=0.5, width=0.9)
+    + geom_boxplot(width=0.15, alpha=0.9, color="#333333", fill="white", size=0.5, outlier_size=3)
+    + scale_fill_manual(values=colors)
+    + labs(title="violin-box · plotnine · pyplots.ai", x="Product Category", y="Customer Satisfaction Score")
+    + 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),
+        legend_position="none",
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/violin-box/metadata/plotnine.yaml

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+library: plotnine
+specification_id: violin-box
+created: '2025-12-30T11:27:24Z'
+updated: '2025-12-30T11:36:46Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595340834
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/violin-box/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/violin-box/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent data design showing four distinct distribution types (normal, right-skewed
+    with outliers, bimodal, uniform)
+  - Clean layering of geom_violin and geom_boxplot demonstrating plotnine grammar
+    of graphics
+  - Good color palette with Python-themed colors that are colorblind-accessible
+  - Perfect title format and professional appearance
+  - Outliers visible in Product B demonstrating complete statistical representation
+  weaknesses:
+  - Does not showcase plotnine-specific features beyond basic geoms (could use faceting,
+    stat_summary, or position adjustments)
+  - Axis labels lack units (e.g., Score (points) or Score (0-100))