feat(pygal): implement scatter-categorical (#2615)

## Implementation: `scatter-categorical` - pygal

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20594557818)*

---------

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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/pygal.py

@@ -0,0 +1,73 @@
+""" pyplots.ai
+scatter-categorical: Categorical Scatter Plot
+Library: pygal 3.1.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pygal
+from pygal.style import Style
+
+
+# Data - Iris-like flower measurements by species
+np.random.seed(42)
+
+# Three species with different characteristic measurements
+species = ["Setosa", "Versicolor", "Virginica"]
+n_per_species = 40
+
+# Setosa: small petals (low x, low y)
+setosa_x = np.random.normal(1.5, 0.25, n_per_species)
+setosa_y = np.random.normal(0.3, 0.1, n_per_species)
+
+# Versicolor: medium petals (medium x, medium y)
+versicolor_x = np.random.normal(4.2, 0.6, n_per_species)
+versicolor_y = np.random.normal(1.3, 0.25, n_per_species)
+
+# Virginica: large petals (high x, high y)
+virginica_x = np.random.normal(5.5, 0.6, n_per_species)
+virginica_y = np.random.normal(2.0, 0.3, n_per_species)
+
+# Custom style for large canvas
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998", "#FFD43B", "#2ca02c"),  # Python Blue, Python Yellow, Green
+    title_font_size=56,
+    label_font_size=40,
+    major_label_font_size=36,
+    legend_font_size=36,
+    value_font_size=28,
+    opacity=0.7,
+    opacity_hover=0.9,
+    stroke_width=0,
+    dots_size=12,
+)
+
+# Create XY scatter chart
+chart = pygal.XY(
+    width=4800,
+    height=2700,
+    style=custom_style,
+    title="scatter-categorical · pygal · pyplots.ai",
+    x_title="Petal Length (cm)",
+    y_title="Petal Width (cm)",
+    show_x_guides=True,
+    show_y_guides=True,
+    legend_at_bottom=False,
+    legend_box_size=24,
+    truncate_legend=-1,
+    dots_size=12,
+)
+
+# Add data for each species as (x, y) tuples
+chart.add("Setosa", list(zip(setosa_x, setosa_y, strict=True)))
+chart.add("Versicolor", list(zip(versicolor_x, versicolor_y, strict=True)))
+chart.add("Virginica", list(zip(virginica_x, virginica_y, strict=True)))
+
+# Save as PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/scatter-categorical/metadata/pygal.yaml

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+library: pygal
+specification_id: scatter-categorical
+created: '2025-12-30T10:38:00Z'
+updated: '2025-12-30T10:45:27Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20594557818
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-categorical/pygal/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of Iris-like data providing a realistic, scientifically meaningful
+    context
+  - Clean three-cluster separation clearly demonstrates categorical grouping
+  - Good color palette (Python colors) that is colorblind-safe
+  - Proper use of pygal custom Style with appropriately scaled font sizes for 4800x2700
+    canvas
+  - Correct title format and descriptive axis labels with units
+  - Alpha transparency (0.7) allows visibility of any overlapping points
+  weaknesses:
+  - Legend positioned in top-left overlaps with the plot area grid region; consider
+    legend_at_bottom=True or repositioning
+  - Marker stroke_width=0 removes outlines which could help distinguish overlapping
+    points