feat(plotnine): implement elbow-curve (#2360)

## Implementation: `elbow-curve` - plotnine

Implements the **plotnine** version of `elbow-curve`.

**File:** `plots/elbow-curve/implementations/plotnine.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528204090)*

---------

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: github-actions[bot]

plots/elbow-curve/implementations/plotnine.py

@@ -0,0 +1,74 @@
+""" pyplots.ai
+elbow-curve: Elbow Curve for K-Means Clustering
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    element_line,
+    element_text,
+    geom_line,
+    geom_point,
+    geom_vline,
+    ggplot,
+    labs,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Simulate realistic K-means inertia values
+# Inertia decreases as k increases, with diminishing returns after optimal k
+np.random.seed(42)
+
+k_values = list(range(1, 11))
+
+# Simulate inertia values that show clear elbow at k=4
+# Formula: base decay curve + noise
+base_inertias = [1000, 500, 280, 150, 120, 100, 85, 75, 68, 62]
+noise = np.random.uniform(-5, 5, len(k_values))
+inertias = [max(10, base + n) for base, n in zip(base_inertias, noise, strict=True)]
+
+# Create DataFrame for plotting
+df = pd.DataFrame({"k": k_values, "inertia": inertias})
+
+# Optimal k (elbow point)
+optimal_k = 4
+
+# Plot
+plot = (
+    ggplot(df, aes(x="k", y="inertia"))
+    + geom_line(color="#306998", size=2, alpha=0.9)
+    + geom_point(color="#306998", size=5, alpha=1.0)
+    + geom_vline(xintercept=optimal_k, linetype="dashed", color="#FFD43B", size=1.5, alpha=0.8)
+    + annotate(
+        "text",
+        x=optimal_k + 0.5,
+        y=inertias[optimal_k - 1] + 80,
+        label=f"Optimal k = {optimal_k}",
+        size=14,
+        color="#FFD43B",
+        ha="left",
+        fontweight="bold",
+    )
+    + labs(
+        title="elbow-curve · plotnine · pyplots.ai",
+        x="Number of Clusters (k)",
+        y="Inertia (Within-Cluster Sum of Squares)",
+    )
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5, alpha=0.3),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/elbow-curve/metadata/plotnine.yaml

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+library: plotnine
+specification_id: elbow-curve
+created: '2025-12-26T19:37:41Z'
+updated: '2025-12-26T19:44:27Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528204090
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/elbow-curve/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/elbow-curve/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Clear visual hierarchy with excellent text sizing at all levels (title, axis labels,
+    ticks)
+  - 'Effective use of plotnine grammar of graphics: layered geoms, annotations, and
+    theme customization'
+  - The yellow dashed vertical line with annotation clearly highlights the optimal
+    k value
+  - Clean, professional appearance using theme_minimal with subtle grid
+  - Good color contrast between the blue data line and yellow annotation
+  weaknesses:
+  - X-axis shows decimal values (2.5, 5.0, 7.5, 10.0) instead of integers (1, 2, 3,
+    ..., 10) which would be more appropriate for discrete k values
+  - The realistic context is somewhat generic - could use a more specific domain scenario
+    in comments