mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks
Mary Beth Kery, Donghao Ren, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Kayur Patel
Abstract
We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of flexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for flexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study findings. Finally, we discuss open challenges in providing flexible code/GUI interactions for data workers.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext fb1675d1-daab-4143-be73-0d29296cfc15Cited by top-tier papers15
- Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output LabelsJochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat et al.CHI 2022 · 70 citations
- Filling typed holes with live GUIsCyrus Omar, David Moon, Andrew Blinn, Ian Voysey et al.PLDI 2021 · 35 citations
- Data Formulator: AI-Powered Concept-Driven Visualization AuthoringChenglong Wang, John Thompson, Bongshin LeeIEEE VIS 2023 · 35 citations
- Falx: Synthesis-Powered Visualization AuthoringChenglong Wang, Yu Feng, Rastislav Bodík, Isil Dillig et al.CHI 2021 · 35 citations
- Dead or Alive: Continuous Data Profiling for Interactive Data ScienceWill Epperson, Vaishnavi Gorantla, Dominik Moritz, Adam PererIEEE VIS 2023 · 28 citations
Builds on1
Related papers
- On the Design of AI-powered Code Assistants for NotebooksAndrew M. McNutt, Chenglong Wang, Robert A. DeLine, Steven Mark DruckerCHI 2023 · 78 citations
- ToonNote: Improving Communication in Computational Notebooks Using Interactive Data ComicsDaye Kang, Tony Ho, Nicolai Marquardt, Bilge Mutlu et al.CHI 2021 · 42 citations
- What's Wrong with Computational Notebooks? Pain Points, Needs, and Design OpportunitiesSouti Chattopadhyay, Ishita Prasad, Austin Z. Henley, Anita Sarma et al.CHI 2020 · 162 citations
- Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning ModelsAmid Ayobi, Jacob Hughes, Christopher J. Duckworth, Jakub J. Dylag et al.CHI 2023 · 26 citations
- How Scientists Use Jupyter Notebooks: Goals, Quality Attributes, and OpportunitiesRuanqianqian (Lisa) Huang, Savitha Ravi, Michael He, Boyu Tian et al.ICSE 2025 · 3 citations
