feat(plotnine): implement scatter-categorical (#2602)

## Implementation: `scatter-categorical` - plotnine

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594556837)*

---------

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

Author: github-actions[bot]

plots/scatter-categorical/implementations/plotnine.py

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+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+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, ggplot, labs, scale_color_manual, theme, theme_minimal
+
+
+# Data
+np.random.seed(42)
+
+# Create sample data with 3 categories showing different patterns
+n_per_group = 40
+
+# Group A: positive correlation
+x_a = np.random.normal(25, 8, n_per_group)
+y_a = x_a * 0.8 + np.random.normal(10, 4, n_per_group)
+
+# Group B: higher values, positive correlation
+x_b = np.random.normal(45, 10, n_per_group)
+y_b = x_b * 0.6 + np.random.normal(15, 5, n_per_group)
+
+# Group C: lower values, weaker correlation
+x_c = np.random.normal(35, 12, n_per_group)
+y_c = np.random.normal(35, 8, n_per_group)
+
+df = pd.DataFrame(
+    {
+        "Temperature (°C)": np.concatenate([x_a, x_b, x_c]),
+        "Growth Rate (cm/week)": np.concatenate([y_a, y_b, y_c]),
+        "Plant Species": (["Species A"] * n_per_group + ["Species B"] * n_per_group + ["Species C"] * n_per_group),
+    }
+)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="Temperature (°C)", y="Growth Rate (cm/week)", color="Plant Species"))
+    + geom_point(size=4, alpha=0.7)
+    + scale_color_manual(values=["#306998", "#FFD43B", "#4ECDC4"])
+    + labs(
+        x="Temperature (°C)",
+        y="Growth Rate (cm/week)",
+        title="scatter-categorical · plotnine · pyplots.ai",
+        color="Plant Species",
+    )
+    + 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),
+        legend_text=element_text(size=16),
+        legend_title=element_text(size=18),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

plots/scatter-categorical/metadata/plotnine.yaml

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+library: plotnine
+specification_id: scatter-categorical
+created: '2025-12-30T10:37:18Z'
+updated: '2025-12-30T10:41:59Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594556837
+issue: 0
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 92
+review:
+  strengths:
+  - Excellent text sizing with all labels clearly readable at target resolution
+  - Clean ggplot2-style grammar of graphics implementation with proper layer composition
+  - Smart use of three distinct data patterns (correlation, high values, scattered)
+    demonstrating categorical differences
+  - Colorblind-safe palette choice with good contrast between categories
+  - Realistic biological scenario (plant species growth vs temperature)
+  weaknesses:
+  - Could leverage more plotnine-specific features like facet_wrap for small multiples
+    or stat_smooth for trend lines
+  - Legend title font size could be slightly larger for consistency with axis titles