Search for: All records

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. 

Interactive visual analysis has many advantages, but an important disadvantage is that analysis processes and workflows cannot be easily stored and reused. This is in contrast to code‐based analysis workflows, which can simply be run on updated datasets, and adapted when necessary. In this paper, we introduce methods to capture workflows in interactive visualization systems for different interactions such as selections, filters, categorizing/grouping, labeling, and aggregation. These workflows can then be applied to updated datasets, making interactive visualization sessions reusable. We demonstrate this specification using an interactive visualization system that tracks interaction provenance, and allows generating workflows from the recorded actions. The system can then be used to compare different versions of datasets and apply workflows to them. Finally, we introduce a Python library that can load workflows and apply it to updated datasets directly in a computational notebook, providing a seamless bridge between computational workflows and interactive visualization tools.