feat(seaborn): implement violin-box

[github-actions]

Implementation: violin-box - seaborn

Implements the seaborn version of violin-box.

File: plots/violin-box/implementations/seaborn.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays four violin plots with embedded box plots comparing Quality Scores (0-100) across four manufacturing batches (A, B, C, D). Each violin uses a distinct color: Batch A in steel blue (#306998), Batch B in yellow (#FFD43B), Batch C in teal (#4A90A4), and Batch D in orange (#E8A838). The violin shapes clearly show different distribution characteristics: Batch A shows a symmetric normal distribution centered around 75, Batch B exhibits a bimodal distribution with two peaks (around 60 and 80), Batch C shows a right-skewed distribution with concentration at lower values, and Batch D displays a left-skewed distribution concentrated at higher values. Each violin contains a visible black box plot with white median markers, quartile boxes, and whiskers. The title "violin-box · seaborn · pyplots.ai" is displayed prominently at the top. X-axis is labeled "Manufacturing Batch" and Y-axis shows "Quality Score (0-100)" with a subtle dashed grid on the y-axis.

Quality Score: 95/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt bold, labels 20pt, ticks 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text, clean spacing between violins
• VQ-03: Element Visibility (8/8) - Violins are well-sized, box plots clearly visible inside
• VQ-04: Color Accessibility (5/5) - Four distinct colors that are colorblind-friendly
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Y-axis has units "(0-100)", X-axis descriptive "Manufacturing Batch"
• VQ-07: Grid & Legend (0/2) - Grid subtle (alpha 0.3) which is good, but no legend present (though not strictly needed as colors match x-axis labels)

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct violin plot with embedded box plot
• SC-02: Data Mapping (5/5) - Categorical on X, numeric on Y
• SC-03: Required Features (5/5) - KDE shape visible, median/quartiles/whiskers shown via box
• SC-04: Data Range (3/3) - Y-axis shows full data range (30-100)
• SC-05: Legend Accuracy (2/2) - No legend needed, colors match x-axis batch labels
• SC-06: Title Format (2/2) - Correct format: "violin-box · seaborn · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Excellent variety: normal (A), bimodal (B), right-skewed (C), left-skewed (D)
• DQ-02: Realistic Context (7/7) - Manufacturing quality scores is a realistic, neutral business scenario
• DQ-03: Appropriate Scale (5/5) - Quality scores 0-100 are realistic for manufacturing QC

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - Only necessary imports (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Uses modern seaborn API with hue parameter correctly
• CQ-05: Output Correct (1/1) - Saves as plot.png with proper dpi

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Excellent use of seaborn's violinplot with inner="box" parameter, proper hue handling to avoid deprecation warnings, saturation parameter for color intensity

Strengths

• Excellent data design showing four distinct distribution shapes (normal, bimodal, right-skewed, left-skewed) that perfectly demonstrate the value of violin plots
• Clean use of seaborn's inner="box" parameter for embedded box plots
• Modern API usage with hue parameter to avoid deprecation warnings
• Professional color palette with good contrast and accessibility
• Appropriate font sizes and clear labeling with units

Weaknesses

• Minor: Grid only on y-axis, though this is acceptable for categorical x-axis
• The legend is disabled (legend=False) which is fine since batch names are on x-axis, but a small legend could enhance the plot for standalone viewing

Verdict: APPROVED