feat(plotly): implement parallel-categories-basic

[github-actions]

Implementation: parallel-categories-basic - plotly

Implements the plotly version of parallel-categories-basic.

File: plots/parallel-categories-basic/implementations/plotly.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a parallel categories diagram visualizing Titanic passenger data across four categorical dimensions. From left to right: Passenger Class (First, Second, Third), Sex (Female, Male), Embarked (Cherbourg, Queenstown, Southampton), and Outcome (Survived, Did Not Survive). Ribbons flow between categories using smooth hspline curves. The color scheme uses Python Blue (#306998) for passengers who did not survive and bright yellow (#FFD43B) for survivors. The title "Titanic Passengers · parallel-categories-basic · plotly · pyplots.ai" is centered at the top. A helpful annotation at the bottom explains the color encoding. The layout is well-balanced with adequate margins, though the "Did Not Survive" label on the far right is slightly truncated.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - All text is clearly readable with appropriate font sizes
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Ribbons are well-sized and flow is clearly visible
• VQ-04: Color Accessibility (5/5) - Blue/yellow contrast is colorblind-safe
• VQ-05: Layout Balance (3/5) - Good proportions but "Did Not Survive" text is slightly cut off on right edge
• VQ-06: Axis Labels (2/2) - Descriptive dimension labels (Passenger Class, Sex, Embarked, Outcome)
• VQ-07: Grid & Legend (0/2) - No traditional legend needed for this plot type; annotation serves as color guide

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct parallel categories plot
• SC-02: Data Mapping (5/5) - All categorical dimensions correctly mapped
• SC-03: Required Features (5/5) - Ribbons show flow, width proportional to count, colored by outcome
• SC-04: Data Range (3/3) - All categories visible and properly ordered
• SC-05: Legend Accuracy (2/2) - Color annotation clearly explains blue=not survived, yellow=survived
• SC-06: Title Format (2/2) - Follows "{context} · {spec-id} · {library} · pyplots.ai" format

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows all aspects: multiple dimensions, varied category sizes, clear survival patterns
• DQ-02: Realistic Context (7/7) - Titanic dataset is a classic, neutral, well-understood real-world dataset
• DQ-03: Appropriate Scale (5/5) - Real passenger data with meaningful category distributions

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data → plot → save
• CQ-02: Reproducibility (1/3) - Uses seaborn dataset which is deterministic, but no explicit seed comment
• CQ-03: Clean Imports (2/2) - Only necessary imports (plotly.graph_objects, seaborn for data)
• CQ-04: No Deprecated API (1/1) - Uses current Plotly API
• CQ-05: Output Correct (1/1) - Saves as plot.png and plot.html

Library Features (5/5 pts)

• LF-01: Uses distinctive library features (5/5) - Uses Parcats with hspline curves, hoveron="color", hoverinfo, arrangement options

Strengths

• Excellent use of Titanic dataset providing realistic, well-known categorical data with meaningful dimensions
• Clear color scheme with blue/yellow contrast that is colorblind-friendly
• Good use of Plotly Parcats with smooth hspline ribbons and interactive hover features
• Title follows correct format with spec-id, library, and pyplots.ai branding
• Helpful annotation explaining color meaning at the bottom of the plot

Weaknesses

• The "Did Not Survive" label on the right side is slightly cut off - consider adjusting margins or abbreviating

Verdict: APPROVED

The implementation excellently demonstrates parallel categories visualization using Plotly's Parcats trace. The Titanic dataset is an ideal choice for this plot type, showing clear patterns across passenger class, sex, embarkation port, and survival outcome. The color scheme effectively highlights survival patterns, and the interactive features (hover) make use of Plotly's strengths. Score of 92/100 meets the ≥90 threshold for immediate approval.