Introducing Pyra: A High-Level Linter for Data Science Software

Pyra is a high-level linter designed to identify code smells in data science software, focusing on anti-patterns that can cause issues in data science pipelines. It offers an easily extensible framework to help developers achieve correct results and improve the reliability of their data science projects. The text introduces Pyra's architecture and execution flow, which involves converting a Jupyter Notebook into a Python script and performing static analysis on the Control Flow Graph (CFG) to infer abstract type information. The tool includes 16 different checkers for detecting code smells, grouped into four categories: misleading visualizations, misleading results, challenges for reproducibility, and general issues. The demo showcases Pyra's capabilities by analyzing a simple data science pipeline, highlighting issues such as inappropriate visualization choices, data leakage, and reproducibility challenges. Pyra provides warnings and suggestions for fixing identified issues, making it a valuable addition to the data science ecosystem. The tool can be easily integrated into existing IDEs as a plugin, offering real-time feedback to developers. This makes Pyra particularly useful for both experienced data scientists and beginners or experts from other fields who may not be familiar with data science best practices.