Fred Hohman
/ Ph.D. Candidate at GT

The Future of Notebook Programming Is Fluid

Mary Beth Kery, Donghao Ren, Kanit Wongsuphasawat, Fred Hohman, Kayur Patel

Abstract

A new kind of widget has begun appearing in the data science notebook programming community that can fluidly switch its own appearance between two representations: a graphical user interface (GUI) tool and plain textual code. Data scientists of all expertise levels routinely work in both visual GUIs (data visualizations or spreadsheets) and plaintext code (numerical, data manipulation, or machine learning libraries). These work tools have typically been separate. Here, we argue for the unique role and potential of fluid GUI/text programming to serve data work practices. We contribute a generalized method and API for robust fluid GUI/text coding in notebooks that addresses key questions in code generation and user-interactions. Finally, we demonstrate the potential of our method in two notebook tool examples and a usability study with professional data science and machine learning practitioners.

Citation

The Future of Notebook Programming Is Fluid
Mary Beth Kery, Donghao Ren, Kanit Wongsuphasawat, Fred Hohman, Kayur Patel
Extended Abstracts on ACM Human Factors in Computing Systems (CHI). Honolulu, HI, USA, 2020.
Project PDF

BibTeX


@inproceedings{kery2020future,
  title={The Future of Notebook Programming Is Fluid},
  author={Kery, Mary Beth and Ren, Donghao and Wongsuphasawat, Kanit and Hohman, Fred and Patel, Kayur},
  booktitle={Proceedings of the 2020 CHI Conference Extended Abstracts on Human Factors in Computing Systems},
  publisher={ACM},
  year={2020}
}