feat(seaborn): implement scatter-annotated (#2798)

## Implementation: `scatter-annotated` - seaborn

Implements the **seaborn** version of `scatter-annotated`.

**File:** `plots/scatter-annotated/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602451004)*

---------

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/scatter-annotated/implementations/seaborn.py

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+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+from adjustText import adjust_text
+
+
+# Set seaborn theme for consistent styling
+sns.set_theme(style="whitegrid", palette="colorblind")
+
+# Data: Technology companies with revenue vs market cap
+np.random.seed(42)
+companies = [
+    "TechCorp",
+    "DataSys",
+    "CloudNet",
+    "AILabs",
+    "CyberSec",
+    "NetFlow",
+    "AppWorks",
+    "CodeBase",
+    "DevOps",
+    "QuantumX",
+    "ByteLogic",
+    "StreamIO",
+    "VirtualAI",
+    "SecureIT",
+    "SmartHub",
+]
+n_points = len(companies)
+
+# Revenue (billions) and Market Cap (billions) - realistic tech company scale
+revenue = np.random.uniform(5, 80, n_points)
+market_cap = revenue * np.random.uniform(2, 8, n_points) + np.random.randn(n_points) * 10
+
+# Create DataFrame for seaborn
+df = pd.DataFrame({"company": companies, "revenue": revenue, "market_cap": market_cap})
+
+# Create plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Use seaborn scatterplot with DataFrame
+sns.scatterplot(
+    data=df,
+    x="revenue",
+    y="market_cap",
+    s=200,
+    alpha=0.7,
+    color=sns.color_palette("colorblind")[0],
+    edgecolor="white",
+    linewidth=1.5,
+    ax=ax,
+)
+
+# Create text annotations with initial offset (collect for adjustText)
+texts = []
+for _, row in df.iterrows():
+    # Start labels slightly offset from points
+    offset_x = 3.0
+    offset_y = 12.0
+    text = ax.text(
+        row["revenue"] + offset_x,
+        row["market_cap"] + offset_y,
+        row["company"],
+        fontsize=14,
+        color="#333333",
+        ha="left",
+        va="bottom",
+    )
+    texts.append(text)
+
+# Use adjustText to prevent label overlaps with connecting lines
+adjust_text(
+    texts,
+    x=df["revenue"].values,
+    y=df["market_cap"].values,
+    arrowprops={"arrowstyle": "-", "color": "#888888", "alpha": 0.6, "lw": 1.0},
+    expand=(1.5, 1.5),
+    force_text=(0.3, 0.3),
+    force_points=(0.3, 0.3),
+    ax=ax,
+)
+
+# Labels and styling
+ax.set_xlabel("Annual Revenue ($ Billion)", fontsize=20)
+ax.set_ylabel("Market Capitalization ($ Billion)", fontsize=20)
+ax.set_title("scatter-annotated · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+
+# Adjust axis limits to accommodate labels
+ax.set_xlim(0, max(revenue) + 15)
+ax.set_ylim(min(market_cap) - 30, max(market_cap) + 60)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/scatter-annotated/metadata/seaborn.yaml

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+library: seaborn
+specification_id: scatter-annotated
+created: '2025-12-30T17:47:43Z'
+updated: '2025-12-30T18:06:41Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602451004
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of adjustText library for automatic label positioning with subtle
+    connecting lines
+  - Proper seaborn theming with whitegrid and colorblind palette
+  - Text sizes optimized for the 4800x2700 resolution
+  - Realistic tech company scenario matching spec application examples
+  - Good marker sizing (s=200) and alpha (0.7) for the data density
+  - Clean, readable code structure following KISS principles
+  weaknesses:
+  - Grid styling could be more subtle (consider reducing grid alpha or using a lighter
+    style)