Computational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re‐run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes. We implement our approach as a JupyterLab extension that supports tracking interactions in Vega‐Altair plots and in a data table view. Persist can re‐execute interaction provenance when a notebook or a cell is re‐executed, enabling reproducibility and re‐use. We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code‐based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.
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B2: Bridging Code and Interactive Visualization in Computational Notebooks
Data scientists have embraced computational notebooks to author analysis code and accompanying visualizations within a single document. Currently, although these media may be interleaved, they remain siloed: interactive visualizations must be manually specified as they are divorced from the analysis provenance expressed via dataframes, while code cells have no access to users' interactions with visualizations, and hence no way to operate on the results of interaction. To bridge this divide, we present B2, a set of techniques grounded in treating data queries as a shared representation between the code and interactive visualizations. B2 instruments data frames to track the queries expressed in code and synthesize corresponding visualizations. These visualizations are displayed in a dashboard to facilitate interactive analysis. When an interaction occurs, B2 reifies it as a data query and generates a history log in a new code cell. Subsequent cells can use this log to further analyze interaction results and, when marked as reactive, to ensure that code is automatically recomputed when new interaction occurs. In an evaluative study with data scientists, we find that B2 promotes a tighter feedback loop between coding and interacting with visualizations. All participants frequently moved from code to visualization and vice-versa, which facilitated their exploratory data analysis in the notebook.
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- NSF-PAR ID:
- 10213186
- Date Published:
- Journal Name:
- Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology
- Page Range / eLocation ID:
- 152 to 165
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3379337.3415851
