feat(seaborn): implement point-basic

[github-actions]

Implementation: point-basic - seaborn

Implements the seaborn version of point-basic.

File: plots/point-basic/implementations/seaborn.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a horizontal point estimate chart showing satisfaction scores (scale 1-10) for six products (A through F). Each product is represented by a blue circular marker (#306998) with horizontal error bars extending to show confidence intervals. The error bars have clear caps at endpoints. A vertical dashed yellow/gold line indicates the overall mean (6.9). The title follows the correct format "point-basic · seaborn · pyplots.ai". X-axis is labeled "Satisfaction Score (1-10)" and Y-axis is labeled "Product". A legend in the lower right explains the overall mean reference line. The plot has a clean white background with subtle vertical grid lines (alpha ~0.3). The layout is well-balanced with the chart filling the canvas appropriately.

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt, labels 20pt, ticks 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Points clearly visible with appropriate marker size (15), error bars thick (3px) with visible caps
• VQ-04: Color Accessibility (5/5) - Blue (#306998) and yellow (#FFD43B) are colorblind-safe, good contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins
• VQ-06: Axis Labels (2/2) - "Satisfaction Score (1-10)" includes units/scale, "Product" is descriptive
• [-] VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), legend well placed but only on x-axis grid which is appropriate; minor: legend could be slightly larger

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct point estimate plot with confidence intervals
• SC-02: Data Mapping (5/5) - Categories on Y-axis, estimates on X-axis (horizontal orientation as spec recommends)
• SC-03: Required Features (5/5) - Points visible, confidence intervals with caps, reference line included as spec suggests
• SC-04: Data Range (3/3) - X-axis 3-10 shows all data with context
• SC-05: Legend Accuracy (2/2) - Legend correctly describes overall mean line
• SC-06: Title Format (2/2) - Uses exact format "point-basic · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows varying confidence interval widths (0.5 to 1.5), points above/below mean, but all CIs are symmetric
• DQ-02: Realistic Context (7/7) - Product satisfaction ratings is a real, neutral business scenario
• DQ-03: Appropriate Scale (4/5) - Satisfaction scores 5.9-8.1 on 1-10 scale are realistic; could show wider range of estimates

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Uses current seaborn API
• [-] CQ-05: Output Correct (0/1) - Saves as 'plot.png' ✓ but missing plt.close()

Library Features (3/5 pts)

• [-] LF-01: Uses distinctive library features (3/5) - Uses sns.pointplot which is seaborn-specific, but then overrides error bars with matplotlib's errorbar. Could better leverage seaborn's built-in CI functionality or use catplot for more seaborn-native approach

Strengths

• Excellent horizontal orientation for category label readability as recommended in spec
• Clear visual hierarchy with well-sized points and error bars with caps
• Helpful reference line showing overall mean with legend explanation
• Good use of varying confidence interval widths to demonstrate uncertainty differences
• Proper title format and axis labeling with units

Weaknesses

• Error bars drawn manually with matplotlib's errorbar instead of using seaborn's native confidence interval features
• All confidence intervals are symmetric around estimates; real-world CIs are often asymmetric

Verdict: APPROVED