Computational notebooks have become a major medium for data exploration and insight communication in data science. Although expressive, dynamic, and flexible, in practice they are loose collections of scripts, charts, and tables that rarely tell a story or clearly represent the analysis process. This leads to a number of usability issues, particularly in the comprehension and exploration of notebooks. In this work, we design, implement, and evaluate Albireo, a visualization approach to summarize the structure of notebooks, with the goal of supporting more effective exploration and communication by displaying the dependencies and relationships between the cells of a notebook using a dynamic graph structure. We evaluate the system via a case study and expert interviews, with our results indicating that such a visualization is useful for an analyst’s self-reflection during exploratory programming, and also effective for communication of narratives and collaboration between analysts.

Related publications:

• John Wenskovitch, Jian Zhao, Scott Carter, Matthew Cooper, and Chris North. “Albireo: An Interactive Tool for Visually Summarizing Computational Notebook Structure,” in 2019 Symposium on Visualization in Data Science. VDS’19. Vancouver, BC, Canada, 2019. Acceptance Rate: 29.6%.