update(box-basic): plotnine — comprehensive quality review and improvement (#4237)

## Summary

Updated **plotnine** implementation for **box-basic**.

**Changes:** Comprehensive quality review — improved data quality,
visual design, code style, and library feature usage.

## 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/box-basic/implementations/plotnine.py

@@ -1,48 +1,122 @@
 """ pyplots.ai
 box-basic: Basic Box 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
+Quality: 92/100 | Created: 2025-12-23
 """
 
 import numpy as np
 import pandas as pd
-from plotnine import aes, element_text, geom_boxplot, ggplot, labs, theme, theme_minimal
+from plotnine import (
+    aes,
+    annotate,
+    coord_cartesian,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_boxplot,
+    ggplot,
+    labs,
+    scale_fill_manual,
+    scale_y_continuous,
+    stat_summary,
+    theme,
+    theme_minimal,
+)
 
 
 # Data
 np.random.seed(42)
-categories = ["Engineering", "Marketing", "Sales", "Support"]
-data = {"category": [], "value": []}
+categories = ["Engineering", "Marketing", "Sales", "Support", "Research"]
+records = []
 
-# Generate salary data for each department with varied distributions
 for cat in categories:
-    n = np.random.randint(50, 150)
+    n = np.random.randint(60, 120)
     if cat == "Engineering":
         values = np.random.normal(95000, 15000, n)
     elif cat == "Marketing":
         values = np.random.normal(75000, 12000, n)
     elif cat == "Sales":
-        values = np.random.normal(70000, 20000, n)
-    else:  # Support
-        values = np.random.normal(55000, 10000, n)
-    data["category"].extend([cat] * n)
-    data["value"].extend(values)
+        base = np.random.normal(68000, 18000, n)
+        outliers = np.random.normal(135000, 6000, 4)
+        values = np.concatenate([base, outliers])
+    elif cat == "Support":
+        values = np.random.normal(55000, 8000, n)
+    else:  # Research
+        values = np.random.normal(85000, 20000, n)
+    records.extend({"department": cat, "salary": v} for v in values)
+
+df = pd.DataFrame(records)
+dept_order = ["Support", "Marketing", "Sales", "Research", "Engineering"]
+df["department"] = pd.Categorical(df["department"], categories=dept_order, ordered=True)
 
-df = pd.DataFrame(data)
+# Compute medians for storytelling annotations
+medians = df.groupby("department", observed=True)["salary"].median()
+eng_median = medians["Engineering"]
+sup_median = medians["Support"]
+gap = eng_median - sup_median
 
-# Plot
+# Custom palette — cohesive muted tones starting from Python Blue
+palette = ["#7FAACC", "#E8A87C", "#D4A5C9", "#82C9B0", "#306998"]
+
+# Plot with stat_summary and annotate for storytelling
 plot = (
-    ggplot(df, aes(x="category", y="value", fill="category"))
-    + geom_boxplot(outlier_size=3, outlier_alpha=0.6, size=0.8)
+    ggplot(df, aes(x="department", y="salary", fill="department"))
+    + geom_boxplot(
+        outlier_size=3.5, outlier_alpha=0.7, outlier_colour="#C0392B", size=0.5, alpha=0.88, width=0.6, color="#444444"
+    )
+    # Median diamond markers via stat_summary — distinctive plotnine feature
+    + stat_summary(fun_y=np.median, geom="point", size=5, shape="D", color="#1a1a1a", fill="#1a1a1a")
+    + scale_fill_manual(values=palette)
+    + scale_y_continuous(labels=lambda vals: [f"${v / 1000:.0f}k" for v in vals], breaks=range(20000, 160001, 20000))
+    + coord_cartesian(ylim=(12000, 156000))
+    # Annotation: salary gap between Engineering and Support
+    + annotate(
+        "text",
+        x=3,
+        y=151000,
+        label=f"Engineering earns ${gap / 1000:.0f}k more than Support",
+        color="#306998",
+        size=10,
+        ha="center",
+        fontweight="bold",
+    )
+    + annotate("segment", x=1, xend=5, y=144000, yend=144000, color="#306998", size=0.6, linetype="dashed", alpha=0.5)
+    # Annotation: Sales outlier cluster callout
+    + annotate(
+        "label",
+        x=3.5,
+        y=132000,
+        label="Senior hires\nabove market rate",
+        size=9,
+        color="#8B0000",
+        fill="#FFF0F0",
+        alpha=0.9,
+        label_size=0,
+        ha="left",
+    )
+    + annotate("segment", x=3.18, xend=3.42, y=132000, yend=132000, color="#C0392B", size=0.5, alpha=0.5)
+    # Annotation: Support tight distribution insight
+    + annotate(
+        "text", x=1, y=28000, label="Narrow spread\n(σ ≈ $8k)", color="#666666", size=8, ha="center", fontstyle="italic"
+    )
     + labs(x="Department", y="Salary ($)", title="box-basic · plotnine · 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"),
+        plot_title=element_text(size=24, color="#1a1a1a", weight="bold", margin={"b": 14}),
+        axis_title_x=element_text(size=20, color="#222222", margin={"t": 14}),
+        axis_title_y=element_text(size=20, color="#222222", margin={"r": 14}),
+        axis_text=element_text(size=16, color="#555555"),
         legend_position="none",
+        panel_grid_major_x=element_blank(),
+        panel_grid_minor=element_blank(),
+        panel_grid_major_y=element_line(color="#e0e0e0", size=0.5),
+        axis_ticks_major_x=element_blank(),
+        axis_ticks_major_y=element_blank(),
+        plot_background=element_rect(fill="white", color="white"),
+        panel_background=element_rect(fill="white", color="white"),
     )
 )