feat(seaborn): implement scatter-categorical

[github-actions]

Implementation: scatter-categorical - seaborn

Implements the seaborn version of scatter-categorical.

File: plots/scatter-categorical/implementations/seaborn.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a categorical scatter plot with Iris-like flower measurement data. Three species (Setosa, Versicolor, Virginica) are displayed using distinct colors: blue (#306998) for Setosa, yellow (#FFD43B) for Versicolor, and green (#6A9F58) for Virginica. The X-axis shows "Petal Length (cm)" ranging from 1-8, and the Y-axis shows "Petal Width (cm)" ranging from 0-3. The title "scatter-categorical · seaborn · pyplots.ai" is displayed at the top in bold. A legend in the upper-left corner identifies the three species. The Setosa cluster appears tightly grouped in the bottom-left (small petals), Versicolor spans the middle region, and Virginica occupies the upper-right (larger petals). A subtle dashed grid is present. Points have white edge borders and slight transparency.

Quality Score: 95/100

Criteria Checklist

Visual Quality (38/40 pts)

• VQ-01: Text Legibility (10/10) - Title 24pt bold, axis labels 20pt, tick labels 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (8/8) - Marker size s=200 with alpha=0.7 is appropriate for 150 data points
• VQ-04: Color Accessibility (5/5) - Blue, yellow, green palette is colorblind-safe
• VQ-05: Layout Balance (5/5) - Plot fills canvas well with balanced margins
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Petal Length (cm)", "Petal Width (cm)"
• VQ-07: Grid & Legend (0/2) - Grid is subtle (alpha=0.3), but dashed linestyle is slightly distracting compared to solid

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot with categorical coloring
• SC-02: Data Mapping (5/5) - X/Y continuous variables correctly assigned
• SC-03: Required Features (5/5) - Distinct colors per category, legend present, alpha transparency for overlap
• SC-04: Data Range (3/3) - All data points visible within axes
• SC-05: Legend Accuracy (2/2) - Legend correctly maps colors to species names
• SC-06: Title Format (2/2) - Uses correct format: "scatter-categorical · seaborn · pyplots.ai"

Data Quality (19/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct clusters with different spreads, but spec suggests varying marker shapes could add distinction
• DQ-02: Realistic Context (7/7) - Classic Iris flower dataset scenario - excellent scientific context
• DQ-03: Appropriate Scale (5/5) - Petal measurements in realistic cm ranges

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) 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 (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses seaborn's scatterplot with hue parameter correctly, but doesn't leverage advanced features like style parameter for marker shapes or seaborn's built-in colorblind palette

Strengths

• Excellent use of the Iris flower dataset context - classic, well-understood scientific scenario
• Perfect text sizing with 24pt title, 20pt labels, 16pt ticks - all highly readable
• Custom colorblind-safe palette with good color contrast between categories
• Clean KISS code structure with proper seed for reproducibility
• Good marker sizing (s=200) and transparency (alpha=0.7) for 150 data points
• White edge borders on markers improve visibility and separation
• Three distinct clusters clearly demonstrate categorical grouping

Weaknesses

• Grid uses dashed linestyle which is slightly more distracting than solid with low alpha
• Does not utilize seaborn's style parameter to vary marker shapes by category as suggested in spec
• Could use seaborn's built-in 'colorblind' palette instead of custom colors

Verdict: APPROVED