feat(pygal): implement bar-categorical (#2678)

## Implementation: `bar-categorical` - pygal

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

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

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595338218)*

---------

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

Author: github-actions[bot]

plots/bar-categorical/implementations/pygal.py

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+""" pyplots.ai
+bar-categorical: Categorical Count Bar Chart
+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 - Raw categorical values (counts computed automatically)
+np.random.seed(42)
+categories = ["Laptop", "Smartphone", "Tablet", "Desktop", "Smartwatch", "Headphones"]
+weights = [0.25, 0.30, 0.15, 0.10, 0.12, 0.08]  # Probability weights for realistic distribution
+raw_data = np.random.choice(categories, size=500, p=weights)
+
+# Count frequencies
+unique, counts = np.unique(raw_data, return_counts=True)
+category_counts = dict(zip(unique, counts, strict=True))
+
+# Sort by count descending for better visualization
+sorted_items = sorted(category_counts.items(), key=lambda x: x[1], reverse=True)
+sorted_categories = [item[0] for item in sorted_items]
+sorted_counts = [item[1] for item in sorted_items]
+
+# Custom style for 4800x2700 canvas
+custom_style = Style(
+    background="white",
+    plot_background="white",
+    foreground="#333333",
+    foreground_strong="#333333",
+    foreground_subtle="#666666",
+    colors=("#306998", "#FFD43B", "#4B8BBE", "#FFE873", "#306998", "#FFD43B"),
+    title_font_size=72,
+    label_font_size=48,
+    major_label_font_size=42,
+    legend_font_size=42,
+    value_font_size=36,
+    value_label_font_size=36,
+    tooltip_font_size=36,
+    stroke_width=2,
+    opacity=0.9,
+    opacity_hover=1.0,
+)
+
+# Create chart
+chart = pygal.Bar(
+    width=4800,
+    height=2700,
+    title="bar-categorical · pygal · pyplots.ai",
+    x_title="Product Category",
+    y_title="Count (Frequency)",
+    style=custom_style,
+    show_legend=False,
+    show_y_guides=True,
+    show_x_guides=False,
+    print_values=True,
+    print_values_position="top",
+    value_formatter=lambda x: str(int(x)),
+    margin=60,
+    spacing=40,
+    truncate_label=-1,
+    x_label_rotation=0,
+)
+
+# Set x-axis labels
+chart.x_labels = sorted_categories
+
+# Add data - single series with counts
+chart.add("Count", sorted_counts)
+
+# Save as PNG and HTML
+chart.render_to_png("plot.png")
+chart.render_to_file("plot.html")

plots/bar-categorical/metadata/pygal.yaml

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+library: pygal
+specification_id: bar-categorical
+created: '2025-12-30T11:23:45Z'
+updated: '2025-12-30T11:36:09Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595338218
+issue: 0
+python_version: 3.13.11
+library_version: 3.1.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/pygal/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/pygal/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/bar-categorical/pygal/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent text legibility with well-scaled font sizes for the 4800x2700 canvas
+  - Clean sorted descending layout makes frequency comparison intuitive
+  - Realistic electronics product category scenario with weighted probability distribution
+  - Value labels on top of each bar enhance readability without cluttering
+  - Proper KISS code structure with reproducible random seed
+  weaknesses:
+  - Could use more distinctive pygal features (e.g., custom tooltips with percentage,
+    value_formatter showing both count and percentage)
+  - Color variety could be added to distinguish high vs low frequency categories