python-dataviz_skill

Overview

This skill provides professional data visualization using Python's matplotlib, seaborn, and plotly. It produces publication-quality static charts (PNG, SVG, PDF) and interactive HTML plots, with sensible defaults and customization for scientific and business use. Use it to generate clear, styled charts, multi-panel figures, and interactive dashboards quickly.

How this skill works

The skill exposes examples, scripts, and utilities built on matplotlib and seaborn for static and statistical plots, and on plotly for interactive visuals. It includes environment setup, reusable patterns for common plot types, and export helpers to save high-resolution PNG/SVG or HTML files. Templates cover distribution, comparison, relationship, heatmap, and interactive chart workflows.

When to use it

• Preparing publication-quality static figures for papers or reports
• Exploring distributions and relationships with statistical plots
• Creating interactive charts or dashboards for web sharing
• Producing multi-panel figures and annotated visuals
• Exporting high-resolution assets (PNG/SVG) or HTML interactive outputs

Best practices

• Set figure size and DPI appropriately (e.g., figsize=(10,6), dpi=300 for print)
• Use seaborn themes and palettes for readable defaults and consistent styling
• Prefer SVG for vector editing and PNG for raster delivery; use tight_layout() to avoid clipping
• Convert dictionary or CSV data to pandas DataFrame for clean plotting pipelines
• Use plotly for interactive needs and export HTML with fig.write_html(); use kaleido for static plotly PNGs

Example use cases

• Create a multi-panel figure comparing experimental conditions (matplotlib + seaborn)
• Draw publication-ready violin and box plots to summarize distributions
• Generate interactive scatter plots with hover tooltips for data exploration (plotly)
• Export a high-resolution correlation heatmap for a methods section
• Build a simple Plotly HTML dashboard for stakeholders to explore KPIs