update(box-basic): seaborn — comprehensive quality review and improvement (#4233)

## Summary

Updated **seaborn** 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/seaborn.py

@@ -1,7 +1,7 @@
 """ pyplots.ai
 box-basic: Basic Box Plot
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 93/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14
+Quality: 94/100 | Created: 2025-12-23
 """
 
 import matplotlib.pyplot as plt
@@ -13,57 +13,123 @@
 # Data
 np.random.seed(42)
 
-categories = ["Engineering", "Marketing", "Sales", "HR", "Finance"]
-data = []
+dept_params = {
+    "Engineering": {"loc": 95000, "scale": 15000, "n": 80},
+    "Marketing": {"loc": 75000, "scale": 12000, "n": 60},
+    "Sales": {"loc": 70000, "scale": 20000, "n": 100},
+    "HR": {"loc": 65000, "scale": 10000, "n": 50},
+    "Finance": {"loc": 85000, "scale": 18000, "n": 70},
+}
 
-for category in categories:
-    # Generate realistic salary distributions with different characteristics
-    if category == "Engineering":
-        values = np.random.normal(95000, 15000, 80)
-    elif category == "Marketing":
-        values = np.random.normal(75000, 12000, 60)
-    elif category == "Sales":
-        values = np.random.normal(70000, 20000, 100)
-    elif category == "HR":
-        values = np.random.normal(65000, 10000, 50)
-    else:  # Finance
-        values = np.random.normal(85000, 18000, 70)
-
-    # Add some outliers
+data = []
+for dept, params in dept_params.items():
+    if dept == "Sales":
+        # Right-skewed distribution to show distributional diversity
+        values = np.random.exponential(scale=15000, size=params["n"]) + 45000
+    else:
+        values = np.random.normal(params["loc"], params["scale"], params["n"])
     outliers = np.random.uniform(values.min() - 20000, values.max() + 25000, 3)
     values = np.concatenate([values, outliers])
-
     for v in values:
-        data.append({"Department": category, "Salary": v})
+        data.append({"Department": dept, "Salary": v})
 
 df = pd.DataFrame(data)
 
+# Seaborn context for global scaling
+sns.set_context("talk", font_scale=1.1)
+
 # Plot
+palette = ["#306998", "#E8A838", "#4CAF50", "#FF7043", "#9C27B0"]
+
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Use hue with palette to avoid seaborn 0.14+ warning
 sns.boxplot(
     data=df,
     x="Department",
     y="Salary",
     hue="Department",
-    palette=["#306998", "#FFD43B", "#4CAF50", "#FF7043", "#9C27B0"],
+    palette=palette,
     linewidth=2.5,
-    fliersize=10,
+    fliersize=0,
     width=0.6,
     legend=False,
     ax=ax,
 )
 
+sns.stripplot(
+    data=df,
+    x="Department",
+    y="Salary",
+    hue="Department",
+    palette=palette,
+    size=5,
+    alpha=0.4,
+    jitter=0.25,
+    legend=False,
+    ax=ax,
+)
+
 # Style
 ax.set_xlabel("Department", fontsize=20)
 ax.set_ylabel("Salary ($)", fontsize=20)
-ax.set_title("box-basic · seaborn · pyplots.ai", fontsize=24)
+ax.set_title("box-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium")
 ax.tick_params(axis="both", labelsize=16)
-ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+ax.yaxis.grid(True, alpha=0.2, linewidth=0.8)
+sns.despine(ax=ax)
 
-# Format y-axis as currency
 ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"${x / 1000:.0f}K"))
 
+# Tighten y-axis to reduce empty space
+y_min = df["Salary"].min() - 5000
+y_max = df["Salary"].max() + 8000
+ax.set_ylim(y_min, y_max)
+
+# Data storytelling: annotate key insights
+medians = df.groupby("Department")["Salary"].median()
+spreads = df.groupby("Department")["Salary"].apply(lambda x: x.quantile(0.75) - x.quantile(0.25))
+
+highest_dept = medians.idxmax()
+widest_dept = spreads.idxmax()
+
+dept_positions = {dept: i for i, dept in enumerate(dept_params.keys())}
+
+# Annotate highest median (Engineering, position 0)
+ax.annotate(
+    f"Highest median: ${medians[highest_dept] / 1000:.0f}K",
+    xy=(dept_positions[highest_dept], medians[highest_dept]),
+    xytext=(dept_positions[highest_dept] + 1.6, y_max - (y_max - y_min) * 0.05),
+    fontsize=13,
+    fontweight="bold",
+    color="#306998",
+    ha="center",
+    arrowprops={"arrowstyle": "->", "color": "#306998", "lw": 1.8, "connectionstyle": "arc3,rad=-0.2"},
+)
+
+# Annotate widest spread (Finance, position 4)
+ax.annotate(
+    f"Widest IQR: ${spreads[widest_dept] / 1000:.0f}K spread",
+    xy=(dept_positions[widest_dept], medians[widest_dept]),
+    xytext=(dept_positions[widest_dept] - 1.6, y_min + (y_max - y_min) * 0.07),
+    fontsize=13,
+    fontweight="bold",
+    color="#9C27B0",
+    ha="center",
+    arrowprops={"arrowstyle": "->", "color": "#9C27B0", "lw": 1.8, "connectionstyle": "arc3,rad=0.2"},
+)
+
+# Annotate right-skewed Sales distribution
+sales_pos = dept_positions["Sales"]
+sales_q3 = df[df["Department"] == "Sales"]["Salary"].quantile(0.75)
+ax.annotate(
+    "Right-skewed\ndistribution",
+    xy=(sales_pos, sales_q3),
+    xytext=(sales_pos + 0.8, sales_q3 + (y_max - y_min) * 0.13),
+    fontsize=13,
+    fontweight="bold",
+    color="#4CAF50",
+    ha="center",
+    arrowprops={"arrowstyle": "->", "color": "#4CAF50", "lw": 1.8},
+)
+
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")