Python Bokeh Requirements

When working with Bokeh, you may find yourself wondering when it's best to use the Bokeh server. This powerful tool allows for quick and simple deployment of interactive data visualizations through effective use of Bokeh server applications.

In certain scenarios, using the Bokeh server can be particularly convenient. For example, if you're working in a Jupyter notebook or building a small app that you and your colleagues can run, the Bokeh server can be a valuable asset. It's also useful for exploratory data analysis, as it allows you to manipulate UI controls and communicate new values to the backend via Bokeh.

Here are a few different scenarios where using the Bokeh server may be beneficial:

* Periodic, timeout, and asynchronous callbacks: These features enable streaming updates and can drive complex behaviors in your application.

* Responding to UI and tool events: The Bokeh server allows you to respond to user input with computations or queries, which can then automatically push server-side updates to the UI elements.

One of the primary purposes of the Bokeh server is to synchronize data between the underlying Python environment and the BokehJS library running in the browser. This flexibility offers some significant advantages, including the ability to integrate other languages, such as R or Scala, with your Bokeh applications.

In addition, using the Bokeh server allows you to decouple your frontend and backend code, making it easier to maintain and update your application over time. By leveraging the power of Python models, such as plots, ranges, axes, and glyphs, you can create high-level abstractions that are easy to work with and understand.

To get started with using the Bokeh server, it's a good idea to familiarize yourself with some of the core concepts of Bokeh. With this knowledge in hand, you'll be well on your way to building interactive web applications that connect front-end UI events to running Python code.

Python bokeh requirements