Arash Asgari, Huan Wu, Amirreza Naziri, Mojtaba Kolahdouzi, Laleh Seyyed-Kalantari. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Jupyter notebooks have become indispensable tools for data analysis and processing in various domains. However, despite their widespread use, there is a notable research gap in understanding and analyzing the contents and code metrics of these notebooks. This gap is primarily attributed to the absence of datasets that encompass both Jupyter notebooks and extracted their code metrics. To address this limitation, we introduce DistilKaggle, a unique dataset specifically curated to facilitate research on code metrics in Jupyter notebooks, utilizing the Kaggle repository as a prime source. Through an extensive study, we identify thirty-four code metrics that significantly impact Jupyter notebook code quality. These features such as lines of code cell, mean number of words in markdown cells, performance tier of developer, etc., are crucial for understanding and improving the overall effectiveness of computational notebooks. The DistilKaggle dataset which is derived from a vast collection of notebooks constitutes two distinct datasets: (i) Code Cells and Markdown Cells Dataset which is presented in two CSV files, allowing for easy integration into researchers' workflows as dataframes. It provides a granular view of the content structure within 542,051 Jupyter notebooks, enabling detailed analysis of code and markdown cells; and (ii) The Notebook Code Metrics Dataset focused on the identified code metrics of notebooks. Researchers can leverage this dataset to access Jupyter notebooks with specific code quality characteristics, surpassing the limitations of filters available on the Kaggle website. Furthermore, the reproducibility of the notebooks in our dataset is ensured through the code cells and markdown cells datasets, offering a reliable foundation for researchers to build upon. Given the substantial size of our datasets, it becomes an invaluable resource for the research community, surpassing the capabilities of individual Kaggle users to collect such extensive data. For accessibility and transparency, both the dataset and the code utilized in crafting this dataset are publicly available at https://github.com/ISE-Research/DistilKaggle.