Shiny vs Panel | Python Tools Comparison

Shiny vs Panel - TLDR

Pros: While still being relatively new for python, Shiny for R has been around for a long time, so that a lot of experiences on that could be put into Shiny for python. Sharing similar concepts should also help R users to migrate. The core of shiny is a reactive programming engine, trying to reduce the required computations as much as possible.
Cons: Shiny uses Bootstrap as its framework for layout and styling - this means you must have understood the concepts of Bootstrap in order to change the UI.
Use (Shiny) if: You don't have time for web development but want to expose your python code as fast as possible.
Alternatives: Dash. Panel.

Pros: Panel allows you to turn jupyter notebooks into dashboards without much additional work. Due to the omnipresence of notebooks this is an easy way to get started fast with a shared dashboard. Also there is built-in support for the many common graph libraries and widgets. You can integrate your panel app into web application frameworks like Django or FastAPI and also use built-in OAuth integration. Panel also supports caching of results on the server side to speed up rendering dashboards.
Cons: The learning curve of Panel is slightly higher than other dashboarding tools, because of the sheer choice of widgets and libraries that are supported and need to be learned as well. The focus of Panel is more on python and less on HTML/CSS.
Use (Panel) if: you want to be flexible about existing widgets and libraries that are supported out of the box and want to make a Jupyter dashboard available for everyone as a web application.
Alternatives: Dash. Jupyter.