# Pynblint: a Static Analyzer for Python Jupyter Notebooks

arXiv: https://arxiv.org/abs/2205.11934 · HTML: https://mlsanity.com/paper/2205.11934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 4 of 4)

- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Eliciting Best Practices for Collaboration with Computational Notebooks](https://mlsanity.com/paper/2202.07233/) (2022)
- [Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists](https://mlsanity.com/paper/2103.10548/) (2021)

## Built on by (most cited first, 3 of 3)

- [Teaching MLOps in Higher Education through Project-Based Learning](https://mlsanity.com/paper/2302.01048/) (2023)
- [Analysing Python Machine Learning Notebooks with Moose](https://mlsanity.com/paper/2509.11748/) (2025)
- [JupOtter: Cell-Level Bug Detection in Jupyter Notebooks](https://mlsanity.com/paper/2606.23877/) (2026)

## Abstract (arXiv)

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.