jupyter-notebook-analysis

What problem does it solve?

This Skill guides practitioners in building comprehensive Jupyter notebook analyses with statistical rigor, robust outlier handling, and publication-ready visualizations, reducing errors and improving reproducibility.

Core Features & Use Cases

• Structured notebook patterns: standardized sections, headers, and reusable templates for data loading, cleaning, analysis, and visualization.
• Statistical checks: recommended tests (Pearson correlation, Mann-Whitney U), p-values, confidence cues, and reporting conventions in plots.
• Publication-ready visuals: guidelines for color palettes, font sizes, and figure captions to ensure print readiness.
• Use Case: Create a notebook that analyzes a dataset, performs correlations, visualizes results, and documents methods for a scientific report.

Quick Start

Create a new notebook and apply the Jupyter Notebook Analysis Patterns template to scaffold data loading, enrichment, analysis, and figure generation.