# KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle

arXiv: https://arxiv.org/abs/2103.10558 · HTML: https://mlsanity.com/paper/2103.10558/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2021.

## Built on by (most cited first, 10 of 10)

- [AnnotatedTables: A Large Tabular Dataset with Language Model Annotations](https://mlsanity.com/paper/2406.16349/) (2024)
- [ADELT: Transpilation Between Deep Learning Frameworks](https://mlsanity.com/paper/2303.03593/) (2023)
- [A Flexible Cell Classification for ML Projects in Jupyter Notebooks](https://mlsanity.com/paper/2403.07562/) (2024)
- [Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production](https://mlsanity.com/paper/2205.11941/) (2022)
- [RuleFlow : Generating Reusable Program Optimizations with LLMs](https://mlsanity.com/paper/2602.09051/) (2026)
- [Predicting the Understandability of Computational Notebooks through Code Metrics Analysis](https://mlsanity.com/paper/2406.10989/) (2024)
- [Themisto: Jupyter-Based Runtime Benchmark](https://mlsanity.com/paper/2504.12365/) (2025)
- [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)
- [PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns](https://mlsanity.com/paper/2608.14742/) (2026)

## Abstract (arXiv)

Computational notebooks have become the tool of choice for many data scientists and practitioners for performing analyses and disseminating results. Despite their increasing popularity, the research community cannot yet count on a large, curated dataset of computational notebooks. In this paper, we fill this gap by introducing KGTorrent, a dataset of Python Jupyter notebooks with rich metadata retrieved from Kaggle, a platform hosting data science competitions for learners and practitioners with any levels of expertise. We describe how we built KGTorrent, and provide instructions on how to use it and refresh the collection to keep it up to date. Our vision is that the research community will use KGTorrent to study how data scientists, especially practitioners, use Jupyter Notebook in the wild and identify potential shortcomings to inform the design of its future extensions.