feat(plotnine): implement scatter-regression-lowess (#2869)

## Implementation: `scatter-regression-lowess` - plotnine

Implements the **plotnine** version of `scatter-regression-lowess`.

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

**Parent Issue:** #2855

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608463131)*

---------

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

Author: github-actions[bot]

plots/scatter-regression-lowess/implementations/plotnine.py

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+""" pyplots.ai
+scatter-regression-lowess: Scatter Plot with LOWESS Regression
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import aes, element_text, geom_point, geom_smooth, ggplot, labs, theme, theme_minimal
+
+
+# Data - create complex non-linear relationship (crop yield vs temperature)
+np.random.seed(42)
+n_points = 150
+
+# Temperature range (x) - realistic agricultural context
+x = np.linspace(5, 35, n_points)
+
+# Yield (y) - peaks around 20-25°C, drops at extremes (realistic crop response)
+# Complex non-linear pattern: quadratic-like with some local variation
+y_base = -0.5 * (x - 22) ** 2 + 80  # Peak around 22°C
+y_noise = np.random.normal(0, 8, n_points)  # Natural variation
+y = y_base + y_noise + 3 * np.sin(x / 3)  # Add subtle local pattern
+
+# Ensure positive yields
+y = np.clip(y, 5, None)
+
+# Create DataFrame
+df = pd.DataFrame({"temperature": x, "yield": y})
+
+# Create plot with scatter points and LOWESS smooth
+plot = (
+    ggplot(df, aes(x="temperature", y="yield"))
+    + geom_point(color="#306998", alpha=0.6, size=3)
+    + geom_smooth(method="lowess", span=0.4, color="#FFD43B", size=2.5, se=False)
+    + labs(
+        x="Temperature (°C)", y="Crop Yield (tons/hectare)", title="scatter-regression-lowess · 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),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300)

plots/scatter-regression-lowess/metadata/plotnine.yaml

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+library: plotnine
+specification_id: scatter-regression-lowess
+created: '2025-12-30T23:52:25Z'
+updated: '2025-12-30T23:54:53Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608463131
+issue: 2855
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-regression-lowess/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent choice of agricultural context (temperature vs crop yield) that naturally
+    demonstrates non-linear LOWESS behavior
+  - Perfect color contrast between blue scatter points and yellow LOWESS curve
+  - Clean, minimal theme with appropriate text sizing for 4800×2700 resolution
+  - Well-structured code following KISS principles
+  - Title format exactly matches specification requirements
+  weaknesses:
+  - The LOWESS curve extends slightly below y=0 at the left edge, which is visually
+    awkward for a yield that should be positive
+  - The span parameter in geom_smooth may not be correctly controlling the LOWESS
+    bandwidth