feat(matplotlib): implement bar-categorical

[github-actions]

Implementation: bar-categorical - matplotlib

Implements the matplotlib version of bar-categorical.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a vertical bar chart with 5 bars representing product categories (Product A through E) on the x-axis. The bars are colored in Python Blue (#306998) with a darker edge color. Each bar has a bold count label above it showing the frequency values: Product A (154), Product B (118), Product C (97), Product D (76), and Product E (55). The bars are sorted in descending order by count. The y-axis shows "Count (Frequency)" ranging from 0 to 160, and the x-axis is labeled "Product Category". The title follows the correct format: "bar-categorical · matplotlib · pyplots.ai". A subtle horizontal dashed grid is visible on the y-axis. The top and right spines are removed for a cleaner appearance.

Quality Score: 94/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt, value labels at 18pt bold - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, bars well-spaced, labels clear
• VQ-03: Element Visibility (8/8) - Bars are appropriately sized, good width relative to spacing
• VQ-04: Color Accessibility (5/5) - Single color scheme (Python Blue), no colorblind issues
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, good proportions
• VQ-06: Axis Labels (1/2) - Descriptive labels but y-axis could have units (e.g., "Count (n)" or just "Count")
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), but no legend needed for this single-series chart

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) - Auto-counts raw categorical data, sorted descending as noted in spec
• SC-04: Data Range (3/3) - Y-axis shows full range from 0 to above max value
• SC-05: Legend Accuracy (2/2) - No legend needed, N/A
• SC-06: Title Format (2/2) - Correct format: "bar-categorical · matplotlib · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows varying frequencies across 5 categories, good distribution variety
• DQ-02: Realistic Context (7/7) - Product preference survey is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (5/5) - 500 survey responses with sensible probability distribution

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) ensures reproducibility
• CQ-03: Clean Imports (2/2) - All imports (matplotlib, numpy, pandas) are used
• CQ-04: No Deprecated API (1/1) - Uses current matplotlib API
• CQ-05: Output Correct (1/1) - Saves as plot.png with correct dpi and bbox_inches

Library Features (2/5 pts)

• LF-01: Uses distinctive library features (2/5) - Uses standard matplotlib bar chart with Axes methods. Added value labels and spine removal are nice touches, but could leverage more matplotlib-specific features like custom tick formatting or annotations.

Strengths

• Excellent text legibility with appropriate font sizes for 4800x2700 output
• Clean KISS code structure following the imports → data → plot → save pattern
• Realistic survey data scenario with varied frequency distribution
• Proper descending sort as suggested in the specification
• Value labels on bars enhance readability and data communication
• Professional appearance with removed top/right spines and subtle grid

Weaknesses

• Could use more distinctive matplotlib features (e.g., custom hatching, gradient fills, or annotation arrows)
• Y-axis label "Count (Frequency)" is slightly redundant - could be simplified

Verdict: APPROVED