Name
Abstract | ||
|---|---|---|
ABSTRACT 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3411764.3445527 | Conference on Human Factors in Computing Systems |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nathaniel Weinman | 1 | 1 | 1.72 |
| steven m drucker | 2 | 2399 | 286.15 |
| Titus Barik | 3 | 96 | 13.38 |
| Robert DeLine | 4 | 2957 | 210.35 |