Pynblint: a Static Analyzer for Python Jupyter Notebooks
This addresses the bottleneck of poor notebook quality for data scientists and ML practitioners, but it is incremental as it builds on existing best practices.
The authors tackled the problem of low-quality Python Jupyter notebooks in ML workflows by developing Pynblint, a static analyzer that checks compliance with best practices and provides recommendations, resulting in a tool that helps improve notebook quality.
Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows. The notebook format, however, has been criticized for inducing bad programming practices; indeed, researchers have already shown that open-source repositories are inundated by poor-quality notebooks. Low-quality output from the prototypical stages of ML workflows constitutes a clear bottleneck towards the productization of ML models. To foster the creation of better notebooks, we developed Pynblint, a static analyzer for Jupyter notebooks written in Python. The tool checks the compliance of notebooks (and surrounding repositories) with a set of empirically validated best practices and provides targeted recommendations when violations are detected.