feat(seaborn): implement scatter-categorical (#2623)

## Implementation: `scatter-categorical` - seaborn

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594554074)*

---------

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

Author: github-actions[bot]

plots/scatter-categorical/implementations/seaborn.py

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+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 95/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - Iris-like flower measurements with species categories
+np.random.seed(42)
+
+# Create data for three flower species with distinct patterns
+n_per_group = 50
+
+# Species A: smaller petals, tight cluster
+species_a_x = np.random.normal(1.5, 0.3, n_per_group)
+species_a_y = np.random.normal(0.3, 0.1, n_per_group)
+
+# Species B: medium petals, wider spread
+species_b_x = np.random.normal(4.5, 0.8, n_per_group)
+species_b_y = np.random.normal(1.4, 0.3, n_per_group)
+
+# Species C: larger petals, elongated cluster
+species_c_x = np.random.normal(5.8, 0.6, n_per_group)
+species_c_y = np.random.normal(2.1, 0.4, n_per_group)
+
+df = pd.DataFrame(
+    {
+        "Petal Length (cm)": np.concatenate([species_a_x, species_b_x, species_c_x]),
+        "Petal Width (cm)": np.concatenate([species_a_y, species_b_y, species_c_y]),
+        "Species": ["Setosa"] * n_per_group + ["Versicolor"] * n_per_group + ["Virginica"] * n_per_group,
+    }
+)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Custom colorblind-safe palette using Python colors first
+custom_palette = ["#306998", "#FFD43B", "#6A9F58"]
+
+sns.scatterplot(
+    data=df,
+    x="Petal Length (cm)",
+    y="Petal Width (cm)",
+    hue="Species",
+    palette=custom_palette,
+    s=200,
+    alpha=0.7,
+    edgecolor="white",
+    linewidth=0.5,
+    ax=ax,
+)
+
+# Styling
+ax.set_title("scatter-categorical · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
+ax.set_xlabel("Petal Length (cm)", fontsize=20)
+ax.set_ylabel("Petal Width (cm)", fontsize=20)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Legend styling
+ax.legend(title="Species", fontsize=16, title_fontsize=18, loc="upper left", framealpha=0.9)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/scatter-categorical/metadata/seaborn.yaml

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+library: seaborn
+specification_id: scatter-categorical
+created: '2025-12-30T10:38:26Z'
+updated: '2025-12-30T10:46:52Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594554074
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 95
+review:
+  strengths:
+  - Excellent use of the Iris flower dataset context - classic, well-understood scientific
+    scenario
+  - Perfect text sizing with 24pt title, 20pt labels, 16pt ticks - all highly readable
+  - Custom colorblind-safe palette with good color contrast between categories
+  - Clean KISS code structure with proper seed for reproducibility
+  - Good marker sizing (s=200) and transparency (alpha=0.7) for 150 data points
+  - White edge borders on markers improve visibility and separation
+  - Three distinct clusters clearly demonstrate categorical grouping
+  weaknesses:
+  - Grid uses dashed linestyle which is slightly more distracting than solid with
+    low alpha
+  - Does not utilize seaborn's style parameter to vary marker shapes by category as
+    suggested in spec
+  - Could use seaborn's built-in 'colorblind' palette instead of custom colors