Python Research Plot

When to use

• Draw figures for papers, reports, posters, or academic slides.
• Refine existing Matplotlib, Seaborn, Plotly, or Pandas charts into cleaner publication-quality visuals.
• Standardize figure style across multiple experiments or subplots.
• Execute figure scripts planned by $paper-figure.

Workflow

1. Clarify the figure's job before choosing a chart type. One figure should answer one question clearly. If aggregated comparison data is not ready, run $analyze-results first.
2. Prefer deterministic Python code over manual editing. Use Matplotlib as the default base and bring in Seaborn only when it genuinely improves the result.
3. Establish the visual system early. Decide font sizes, line widths, marker sizes, tick density, and color palette before polishing details. If multi-figure consistency is required, use $matplotlib-base-style for non-data defaults.
4. Design for the final medium. Paper figures need print-safe sizing and grayscale robustness; slides can use stronger contrast and fewer annotations.
5. Export both editable and delivery formats when possible. Prefer PDF or SVG for vector output and PNG for quick review.