(As of 11/2019)

Panel is a stable library at this point in time and the major roadmap items mainly include adding new components and making it easier to achieve a polished look and feel for complicated applications. The main roadmap items icnlude:

  1. Other plotting & widget libraries: Panel already supports a wide variety of libraries, including all the libraries currently in use by the authors or their collaborators. Most other libraries can trivially be supported as well, if anyone can provide an example of a plot already supported by Panel converted into this other library. As mentioned in issue 2, we will be working on bringing ipywidgets-based components to Panel.

  2. Themes and Templates: By default, Panel apps use Bokeh’s default theming, but other look-and-feel options can be provided by using other available Bokeh themes, making your own, or embedding into a Bokeh Jinja2 template. We’ll add examples of such theming and provide a range of custom templates to achieve a polished look and feel in the coming releases.

  3. BI tools: Panel is designed for displaying content that you already have developed in a Jupyter notebook, but it could also be used as a way for building business-intelligence-style dashboards that mix traditional Python plotting output with BI-style indicators like speedometers, single number displays (e.g. stock tickers), and so on. Adding a small number of such plots or widgets could help make it simpler to build BI-type dashboards where the content comes directly from Python.

  4. Jupyter notebook extension: The Jupyter-Dashboards project provided a Jupyter extension allowing drag and drop layout for Jupyter notebook cells. This approach worked well in many cases, but the underlying server used for deployment is no longer maintained, and so that approach remains only a proof of concept. Panel, on the other hand, is fully deployable outside of the notebook as well as in it, and so it would be great to have a drag-and-drop interface either based on the Jupyter-Dashboards project or developed separately to provide similar capabilities. As for Jupyter Dashboards, the layout information could be stored as cell metadata in the notebook, providing hints for Panel to set up the dashboard layout when served separately. There are likely to be tricky issues with control flow that would need to be addressed when a notebook is used in a non-linear dashboard in this way.

  5. Support for Traitlets: Panel currently supports declarative user interfaces for objects defined using the Param library, and similar support could be added for objects defined using the Traitlets library. Traitlets and Param offer similar functionality, so the benefit of adding Traitlets support would primarily be for users who have large bodies of code already written as traitlets.

If any of the functionality above is interesting to you (or you have ideas of your own!) and can offer help with implementation, please open an issue on this repository. And if you are lucky enough to be in a position to fund our developers to work on it, please contact