PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns

[2608.14742] PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Software Engineering arXiv:2608.14742 (cs) [Submitted on 13 Aug 2026] Title: PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns Authors: Syrym Abdikhan , Mazhar Hameed View a PDF of the paper titled PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns, by Syrym Abdikhan and Mazhar Hameed View PDF HTML (experimental) Abstract: Pandas has emerged as the de facto library for data processing and machine learning, widely used for tasks, such as data loading, transformation, and analysis. Despite its ubiquity, there has been limited systematic investigation into how Pandas is used in real-world projects and how typical workflows are composed in practice. To address this gap, we introduce PandasCorpus, a dataset curated from GitHub repositories that captures real-world Pandas workflows at scale. In this work, a workflow refers to Pandas-based code contained in Jupyter notebooks, a prevalent medium for writing, executing, and sharing data analysis code. The dataset comprises 139k notebooks from approximately 100k repositories and captures more than 4M Pandas API calls spanning 136 distinct operations. Beyond dataset construction, we characterize workflows using structural and Pandas-specific features and analyze notebook evolution between 2015 and 2025. Our study examines code executability, notebook size, and recurring sequences of Pandas operations, providing empirical insights into how Pandas is used in practice. The resulting corpus offers a reusable resource for studying data analysis workflows, Pandas usage patterns, and library-aware code composition. Both the dataset and the extraction pipeline are publicly available via GitHub and Zenodo. Subjects: Software Engineering (cs.SE) ; Machine Learning (cs.LG) Cite as: arXiv:2608.14742 [cs.SE] (or arXiv:2608.14742v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.14742 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Mazhar Hameed [ view email ] [v1] Thu, 13 Aug 2026 21:23:58 UTC (396 KB) Full-text links: Access Paper: View a PDF of the paper titled PandasCorpus: A Resource of Real-World Pandas Workflows and Usage Patterns, by Syrym Abdikhan and Mazhar Hameed View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE < prev | next > new | recent | 2026-08 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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