Bokeh 2.3.3 Jun 2026

Data visualization bridges the gap between complex data analysis and actionable insights. In the Python ecosystem, the Bokeh library stands out for its unique ability to generate interactive, web-ready visualizations directly from Python code. While the library continues to evolve, Bokeh 2.3.3 remains a highly stable, widely deployed version in enterprise environments, legacy pipelines, and production dashboards.

Creating modern, responsive user interfaces requires combining multiple visual layers into a grid system. Bokeh 2.3.3 features dedicated layout functions found in bokeh.layouts : row() : Places plots side-by-side horizontally. column() : Aligns plots and widgets sequentially downward.

: Bokeh 2.3.x supports rendering mathematical notation (LaTeX) via MathJax through the MathText model. bokeh 2.3.3

Resolved issues where layout structures inside Panel objects were not rendering correctly.

: It supports multiple output formats, including standalone HTML files, server-side applications via the Bokeh Server , and integration within Jupyter Notebooks . Data visualization bridges the gap between complex data

: Solved a masking issue where the dropdown choice menu became completely hidden inside MultiChoice component widgets.

: Under custom themes, Y-axis label formatting often drifted or over-pivoted text strings, making raw financial or scientific tick designations unreadable. Version 2.3.3 repaired the formatting inheritance pipeline to preserve string manipulation choices. : Bokeh 2

: Layout hierarchy rendering regressions that caused multi-plot dashboards constructed via downstream frameworks like Panel to overlap or clip during dynamic data refreshes were resolved. 3. Axis Label and Dropdown UI Adjustments

Bokeh 2.3.3 is a powerful and feature-rich library for creating interactive visualizations and dashboards. With its improved performance, enhanced HoverTool, and new color palette, Bokeh 2.3.3 provides a comprehensive platform for data scientists and developers to create stunning visuals. Whether you're working with big data, creating dashboards, or simply exploring data, Bokeh 2.3.3 is an ideal choice. Try it out today and unlock the full potential of your data!

The figure() function is your primary interface for creating a plot. It returns a Figure object, which serves as the canvas for your visualization. When you create a figure, you can specify its dimensions, the tools that will be available in the toolbar (such as pan, zoom, box select, and reset), and the ranges and labels for the x and y axes.

The Bokeh community is active and welcoming. For users of Bokeh 2.3.3, the following resources are available: