Skill UI

Seaborn is a powerful Python library for creating publication-quality statistical graphics. It is designed for dataset-oriented plotting, making it ideal for exploratory data analysis, multivariate analysis, and visualizing complex relationships, distributions, and comparisons with minimal code. It builds upon Matplotlib, offering high-level interfaces for functions like scatterplots, heatmaps, and pair plots.

Matplotlib is a foundational Python library for creating static, animated, and interactive plots with full customization. It supports various plot types like line, scatter, bar, and heatmaps, suitable for scientific and statistical visualizations. Users can customize appearance, create multi-panel figures, and export to PNG, PDF, or SVG. Ideal for publication-quality graphics and integrating with Jupyter notebooks.

Seaborn is a Python visualization library built on matplotlib for creating statistical graphics. It integrates with pandas for dataset-oriented plotting, offering publication-quality defaults for distributions, relationships, and categorical comparisons. Ideal for exploratory data analysis, multivariate analysis, and complex multi-panel figures. Supports various plot types like box plots, violin plots, and heatmaps.

Generate inline SVG graphics using DAX measures that return SVG markup strings for Power BI reports. Supports table, matrix, card, image, and slicer visuals. Includes templates for common chart types and guidance on using UDF libraries.

A comprehensive guide for creating self-contained, high-quality tables and figures for demography manuscripts. It outlines best practices for generating distinctive demographic graphics—such as population pyramids, Lexis surfaces, and survival curves—ensuring they are accessible, reproducible, and adhere to journal limits.

Provides comprehensive guidelines for constructing self-contained, highly legible tables and figures for population and development research manuscripts. It emphasizes that exhibits must communicate not only raw demographic patterns but also their broader development and policy significance to a wide, non-specialist readership (e.g., economists, environmental scholars). Key principles include showing uncertainty, using appropriate specialized graphics (e.g., population pyramids, survival curves), and maintaining data reproducibility.

A comprehensive guide for structuring research papers, particularly in computer graphics and visualization fields (like SIGGRAPH). It dictates how to match technical claims (e.g., 'higher quality,' 'faster') to verifiable evidence, including head-to-head comparisons against State-of-the-Art (SOTA), detailed metric reporting (PSNR, SSIM, Hausdorff), performance timing linked to specific hardware, and robust user studies. Essential for achieving academic rigor and pre-empting domain reviewer objections.

A comprehensive guide detailing the standards for scientific reproducibility in visualization research, particularly for IEEE VIS submissions. It outlines the Graphics Replicability Stamp Initiative (GRSI) requirements, covering how independent volunteers must reproduce results using provided code, data, and environments (e.g., Docker containers). Learn how to package your work—including determinism aids, mapping tables, and licensed data—to pass rigorous, clean-machine testing.

Seaborn is a high-level Python visualization library designed for creating publication-quality statistical graphics. It facilitates dataset-oriented plotting, multivariate analysis, and complex statistical visualizations (like KDEs, distributions, and relationships) directly from Pandas DataFrames with minimal code. It builds upon Matplotlib to provide semantic mapping and statistical awareness.