jupyter-notebook-analysis

Structure your notebook with standardized sections for data loading, cleaning, analysis, and visualization using the Jupyter Notebook Analysis Patterns template. This enforces reproducibility, statistical rigor, and publication-ready formatting across multi-cell workflows with numpy, pandas, and matplotlib.

Include recommended statistical tests like Pearson correlation and Mann-Whitney U with p-values and confidence reporting. The Skill enforces statistical rigor by guiding test selection, results documentation, and proper reporting conventions for correlation analyses and outlier handling.

Apply guidelines for color palettes, font sizes, and figure captions within your notebook's visualization sections. The Skill ensures plots meet publication standards through structured templates and best practices for clear visual communication alongside your analysis.

Yes. The Skill applies to multi-cell notebook workflows, enforcing structured templates and documentation practices that improve reproducibility. It combines data loading, cleaning, analysis, and methods sections to create auditable, reusable analyses.

Use the Skill's structured template with dedicated sections for data cleaning and robust outlier handling procedures. It guides practitioners through cleaning workflows before analysis while maintaining transparency and reproducibility in your notebook documentation.