feat(pygal): implement bar-categorical

[github-actions]

Implementation: bar-categorical - pygal

Implements the pygal version of bar-categorical.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a vertical bar chart showing product category frequencies. Six blue bars (#306998) represent different electronics categories: Smartphone (142), Laptop (130), Tablet (74), Smartwatch (62), Desktop (48), and Headphones (44). The bars are sorted in descending order by count. Each bar has its count value displayed on top. The title "bar-categorical · pygal · pyplots.ai" appears at the top. The x-axis is labeled "Product Category" and y-axis is labeled "Count (Frequency)". Horizontal grid lines (dotted, subtle) help read values. White background with clean layout.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title, axis labels, tick labels, and value labels all clearly readable at full resolution
• VQ-02: No Overlap (8/8) - No overlapping text; category labels well-spaced, values above bars don't collide
• VQ-03: Element Visibility (8/8) - Bars are well-sized and clearly visible with good spacing between them
• VQ-04: Color Accessibility (5/5) - Single color scheme (blue), no color differentiation needed for this single-series chart
• VQ-05: Layout Balance (4/5) - Good use of canvas space, plot fills appropriate area; slight whitespace on right
• VQ-06: Axis Labels (2/2) - Both axes have descriptive labels: "Product Category" and "Count (Frequency)"
• VQ-07: Grid & Legend (0/2) - Grid is appropriately subtle, but legend is hidden (show_legend=False) which is correct for single series

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct bar chart for categorical count data
• SC-02: Data Mapping (5/5) - Categories on x-axis, counts on y-axis as specified
• SC-03: Required Features (5/5) - Automatically counts raw categorical data, sorted by count descending
• SC-04: Data Range (3/3) - Y-axis shows full range from 0 to beyond max count (142)
• SC-05: Legend Accuracy (2/2) - Legend appropriately hidden for single-series data
• SC-06: Title Format (2/2) - Correct format: "bar-categorical · pygal · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows varying frequencies across categories demonstrating the count concept; could show more extreme variation
• DQ-02: Realistic Context (7/7) - Electronics product categories is a neutral, realistic business scenario
• DQ-03: Appropriate Scale (4/5) - 500 samples with realistic probability weights; counts are reasonable

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → style → chart → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (numpy, pygal, Style)
• CQ-04: No Deprecated API (1/1) - Uses current pygal API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses pygal's Style customization and print_values feature, but could leverage more pygal-specific features like tooltips or animations in HTML output

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

Verdict: APPROVED