  Computational notebooks, such as Jupyter Notebook, have become data
scientists' de facto programming environments. Many visualization researchers
and practitioners have developed interactive visualization tools that support
notebooks, yet little is known about the appropriate design of these tools. To
address this critical research gap, we investigate the design strategies in
this space by analyzing 163 notebook visualization tools. Our analysis
encompasses 64 systems from academic papers and 105 systems sourced from a pool
of 55k notebooks containing interactive visualizations that we obtain via
scraping 8.6 million notebooks on GitHub. Through this study, we identify key
design implications and trade-offs, such as leveraging multimodal data in
notebooks as well as balancing the degree of visualization-notebook
integration. Furthermore, we provide empirical evidence that tools compatible
with more notebook platforms have a greater impact. Finally, we develop
SuperNOVA, an open-source interactive browser to help researchers explore
existing notebook visualization tools. SuperNOVA is publicly accessible at:
https://poloclub.github.io/supernova/.
