Nathaniel Weinman
University of California, Berkeley, Berkeley, California, United States
Computational notebooks, which seamlessly interleave code with results, have become a popular tool for data scientists due to the iterative nature of exploratory tasks. However, notebooks provide a single execution state for users to manipulate through creating and manipulating variables. When exploring alternatives, data scientists must carefully create many-step manipulations in visually distant cells. We conducted formative interviews with 6 professional data scientists, motivating design principles behind exposing multiple states. We introduce forking --- creating a new interpreter session --- and backtracking --- navigating through previous states. We implement these interactions as an extension to notebooks that help data scientists more directly express and navigate through decision points a single notebook. In a qualitative evaluation, 11 professional data scientists found the tool would be useful for exploring alternatives and debugging code to create a predictive model. Their insights highlight further challenges to scaling this functionality.
https://doi.org/10.1145/3411764.3445527
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)