Computational notebooks help data analysts analyze and visualize datasets, and share analysis procedures and outputs. However, notebooks typically combine code (e.g., Python scripts), notes, and outputs (e.g., tables, graphs). The combination of disparate materials is known to hinder the comprehension of notebooks, making it difficult for analysts to collaborate with other analysts unfamiliar with the dataset. To mitigate this problem, we introduce ToonNote, a JupyterLab extension that enables the conversion of notebooks into “data comics.’’ ToonNote provides a simplified view of a Jupyter notebook, highlighting the most important results while supporting interactive and free exploration of the dataset. This paper presents the results of a formative study that motivated the system, its implementation, and an evaluation with 12 users, demonstrating the effectiveness of the produced comics. We discuss how our findings inform the future design of interfaces for computational notebooks and features to support diverse collaborators.
DaYe Kang, Tony Hoi, Nicolai Marquardt, Bilge Mutlu, and Andrea BIanchi. 2021. ToonNote: Improving Communication in Computational Notebooks Using Interactive Data Comics. To appear in CHI 2021.