Case Study Comparison of Computational Notebook Platforms for Interactive Visual Analytics

The way of using computational notebooks is quite different between data science and visual analytics. Data scientists focus on data exploration with the code, while visual analytics users are interested in engaging with interactive visual interfaces to facilitate analytical reasoning. Such a difference leads to design contradictions while merging visual analytics tools with data science tools in computational notebooks. In this work, we investigated the problem using an example called “Andromeda,” which is an interactive dimension reduction algorithm, and implemented it using three different notebook platforms: 1) Python code in a Jupyter Notebook, 2) JavaScript code in an Observable Notebook, and 3) embedding both Python (data science use) and JavaScript (visual analytics use) in a Jupyter Notebook. Advantages and disadvantages are concluded for each platform by making comparisons based on various aspects, such as design logic, coding differences, performance, and usability. Laying the groundwork for data scientists, advice and recommendations are made on architecting similar notebooks and which platform to choose in various situations.