Pynblint: a Static Analyzer for Python Jupyter Notebooks
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 24 May 2022.
Builds on
4 of 8 references.
References · 4
- 2021Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Processcited by 56
- 2019Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebookscited by 13
- 2022Eliciting Best Practices for Collaboration with Computational Notebookscited by 4
- 2021Towards Productizing AI/ML Models: An Industry Perspective from Data Scientistscited by 2
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2026JupOtter: Cell-Level Bug Detection in Jupyter Notebooks22 Jun 2026
- 2025Analysing Python Machine Learning Notebooks with Moose15 Sep 2025
- 2023Teaching MLOps in Higher Education through Project-Based Learning2 Feb 2023