Comparison of Computational Notebook Platforms for Interactive Visual Analytics: Case Study of Andromeda Implementations

Existing notebook platforms have different capabilities for supporting visual analytics use. It is not clear which platform to choose for implementing visual analytics notebooks. In this work, we investigated the problem using Andromeda, an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python-based Jupyter Notebook, 2) JavaScript-based Observable Notebook, and 3) Jupyter Notebook embedding both Python (data science use) and JavaScript (visual analytics use). We also made comparisons for all the notebook platforms via a case study based on metrics such as programming difficulty, notebook organization, interactive performance, and UI design choice. Furthermore, guidelines are provided for data scientists to choose one notebook platform for implementing their visual analytics notebooks in various situations. Laying the groundwork for future developers, advice is also given on architecting better notebook platforms.