feat(matplotlib): implement scatter-categorical (#2604)

## Implementation: `scatter-categorical` - matplotlib

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594554566)*

---------

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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/matplotlib.py

@@ -0,0 +1,80 @@
+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+Library: matplotlib 3.10.8 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-30
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# Data - Iris-like measurements with species categories
+np.random.seed(42)
+
+# Generate data for 3 plant species with different characteristics
+n_per_group = 40
+
+# Species A: Smaller petals
+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
+species_b_x = np.random.normal(4.0, 0.5, n_per_group)
+species_b_y = np.random.normal(1.3, 0.2, n_per_group)
+
+# Species C: Larger petals
+species_c_x = np.random.normal(5.5, 0.6, n_per_group)
+species_c_y = np.random.normal(2.0, 0.3, n_per_group)
+
+# Colors - Python Blue, Python Yellow, and a complementary teal
+colors = ["#306998", "#FFD43B", "#2AA198"]
+categories = ["Species A", "Species B", "Species C"]
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Plot each category with distinct colors and markers
+ax.scatter(
+    species_a_x,
+    species_a_y,
+    s=200,
+    c=colors[0],
+    alpha=0.8,
+    label=categories[0],
+    marker="o",
+    edgecolors="white",
+    linewidths=0.5,
+)
+ax.scatter(
+    species_b_x,
+    species_b_y,
+    s=200,
+    c=colors[1],
+    alpha=0.8,
+    label=categories[1],
+    marker="s",
+    edgecolors="#666666",
+    linewidths=0.5,
+)
+ax.scatter(
+    species_c_x,
+    species_c_y,
+    s=200,
+    c=colors[2],
+    alpha=0.8,
+    label=categories[2],
+    marker="^",
+    edgecolors="white",
+    linewidths=0.5,
+)
+
+# Labels and styling
+ax.set_xlabel("Petal Length (cm)", fontsize=20)
+ax.set_ylabel("Petal Width (cm)", fontsize=20)
+ax.set_title("scatter-categorical · matplotlib · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+ax.legend(fontsize=16, loc="upper left", framealpha=0.9)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/scatter-categorical/metadata/matplotlib.yaml

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+library: matplotlib
+specification_id: scatter-categorical
+created: '2025-12-30T10:37:25Z'
+updated: '2025-12-30T10:42:25Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594554566
+issue: 0
+python_version: 3.13.11
+library_version: 3.10.8
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/matplotlib/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/matplotlib/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent use of different marker shapes (circle, square, triangle) for additional
+    category distinction beyond color
+  - Clean, well-organized code following KISS principles
+  - Realistic Iris-like data scenario that is scientifically relevant and neutral
+  - White edge colors on markers improve visibility and separation
+  - Proper font sizing throughout for high-resolution output
+  weaknesses:
+  - Library features could be more distinctive - matplotlib offers patheffects, custom
+    marker paths, or gradient fills that are not utilized
+  - Yellow markers with white background could have slightly better contrast