Bokeh 2.3.3 ((full)) -

Bokeh 2.3.3: A Deep Dive into the Stable Workhorse of Interactive Data Visualization

In the fast-evolving world of data science, it's easy to get caught up in the latest releases, beta features, and breaking changes. However, seasoned developers and data engineers know the immense value of a stable, well-tested release. Enter Bokeh 2.3.3—a version that, while not the absolute newest, represents a golden standard for reliability, performance, and production-ready interactive visualization.

Fixing a critical regression where LabelSet would crash if the angle field was not provided in the data source.
Resolving issues with tooltips not displaying correctly on hover for certain glyphs (especially VBar and HBar).
Correcting a JavaScript error that occurred when using DataRange1D with categorical data.
Fixing a bug where Plot background fill color changes weren't properly reflected in the generated HTML.
Minor documentation corrections and example updates.

To get started with Bokeh, you'll need to have Python installed on your machine. Then, you can install Bokeh using pip: bokeh 2.3.3

Python developers utilize Bokeh to build high-performance, interactive visualizations directly for modern web browsers without needing to write client-side JavaScript. Version 2.3.3 secures this workflow by ensuring that the browser-based client (BokehJS) interprets Python commands predictably and uniformly. 📈 Key Bug Fixes & Improvements Bokeh 2

Advanced Features and Use Cases

Installation

pip install bokeh==2.3.3

Have you used Bokeh 2.3.3 in production? Share your experience or migration story in the comments below. Fixing a critical regression where LabelSet would crash

Customization: The library provides low-level control over every visual element, from glyph properties to layout positioning, while also offering high-level "charts" for quick data exploration.

12. Official Docs for 2.3.3

Archived docs:
https://docs.bokeh.org/en/2.3.3/