Bokeh 2.3.3 Jun 2026

To get started, you need to install Bokeh 2.3.3 into your Python environment. It is best practice to use a virtual environment to avoid conflicts with other packages. Installation via pip

By following this guide, you'll be well on your way to creating stunning visualizations with Bokeh 2.3.3. Happy visualizing!

Even as a patch release, Bokeh 2.3.3 inherits the full feature set of the 2.3 series. Some of the most notable features include:

output_file() : Saves the generated plot as a standalone HTML file on your local disk. bokeh 2.3.3

The Bokeh Discourse is a valuable resource for understanding user experiences. Feedback on version 2.3.3 highlights both its strengths and areas where users encountered issues.

Bokeh is an interactive visualization library designed for modern web browsers. It provides developers and data scientists with the tools to create elegant, concise, and versatile graphics, while delivering high-performance interactivity across large or streaming datasets. The library's goal is to enable the construction of novel graphics in the style of D3.js, but with the convenience and power of Python, all without requiring the user to write any JavaScript.

As a maintenance release within the 2.3.x line, 2.3.3 offers enhanced stability, making it a stable choice for production environments, including Google Colaboratory . To get started, you need to install Bokeh 2

For scatter plots with tens or hundreds of thousands of points, Bokeh 2.3.3 includes refinements to the WebGL rendering backend. Markers no longer flicker when panning and zooming, and performance degradation over time (memory leaks) was significantly reduced.

# --- 2. Setting up the Bokeh Figure --- output_file("crowd_roar_analysis.html")

Data scientists write clean, native Python code to define plots, glyphs, data sources, and interactions. Happy visualizing

circles = p.circle('date', 'price', source=source, size=4, color="navy", alpha=0.3)

Bokeh is designed to handle with high performance. By using a ColumnDataSource (a column‑oriented data structure) and Bokeh’s web‑based rendering, plots remain responsive even with millions of points.