feat(plotnine): implement scatter-annotated (#2797)

## Implementation: `scatter-annotated` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602453182)*

---------

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

Author: github-actions[bot]

plots/scatter-annotated/implementations/plotnine.py

@@ -0,0 +1,68 @@
+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_text,
+    geom_point,
+    geom_text,
+    ggplot,
+    labs,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Company performance metrics (fewer points to avoid label overlap)
+np.random.seed(42)
+
+companies = [
+    "TechCorp",
+    "DataFlow",
+    "CloudNet",
+    "AI Labs",
+    "InfoSys",
+    "AppWorks",
+    "SoftCore",
+    "CodeBase",
+    "SmartSys",
+    "WebDev",
+]
+
+# Revenue (millions) and Profit Margin (percentage) - spread out to avoid overlap
+revenue = np.array([35, 95, 140, 200, 65, 170, 110, 55, 125, 80])
+profit_margin = np.array([6, 12, 18, 23, 9, 20, 15, 8, 16, 11])
+
+# Add small variation
+revenue = revenue + np.random.uniform(-3, 3, len(companies))
+profit_margin = profit_margin + np.random.uniform(-0.5, 0.5, len(companies))
+
+df = pd.DataFrame({"company": companies, "revenue": revenue, "profit_margin": profit_margin})
+
+# Create plot
+plot = (
+    ggplot(df, aes(x="revenue", y="profit_margin"))
+    + geom_point(size=6, alpha=0.7, color="#306998")
+    + geom_text(aes(label="company"), size=11, nudge_y=0.9, color="#333333", va="bottom")
+    + labs(x="Annual Revenue ($ Millions)", y="Profit Margin (%)", title="scatter-annotated · plotnine · pyplots.ai")
+    + scale_x_continuous(limits=(20, 220))
+    + scale_y_continuous(limits=(4, 26))
+    + 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),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/scatter-annotated/metadata/plotnine.yaml

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+library: plotnine
+specification_id: scatter-annotated
+created: '2025-12-30T17:47:43Z'
+updated: '2025-12-30T17:53:48Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602453182
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent label placement with nudge_y preventing overlap
+  - Clean minimal theme appropriate for annotated scatter plot
+  - Well-chosen data spread prevents label collisions
+  - Proper use of plotnine grammar of graphics syntax
+  - Appropriate font sizing for 4800x2700 resolution
+  weaknesses:
+  - Grid lines could be more subtle (add alpha parameter if possible)
+  - Point size could be slightly larger for only 10 data points (size=8-10 would be
+    more visible)