python-conventions

To standardize Python experiment scripts for reproducibility, enforce consistent header blocks documenting Goals, Input, and Output, use repo-root aware path handling, and apply shared figure styling via a configuration file.

The best way to manage paths in Python data analysis scripts is using a repo-root aware approach. This makes scripts portable and deterministic by anchoring path handling to the repository root directory.

To keep figure styling consistent across Python experiments, reference a shared configuration file for sizing, DPI, and style. This ensures visual consistency across all scripts generating figures.

No, you do not need an external framework to enforce these coding standards. The conventions rely only on standard data science libraries and require a markdown frontmatter file defining the skill name and description.

A Python script header block for data analysis should include Goals, Input, and Output sections. This documents the script's intent and data flow to improve readability and traceability for experiment scripts.

Standardized conventions for research experiment scripts improve readability, traceability, and reproducibility across projects. This makes onboarding new team members easier and ensures results can be reproduced consistently.