To verify that the installation was successful and you are using the correct version, run this quick Python snippet:
is a targeted patch release within the 2.x lifecycle of the Bokeh data visualization library . Released in July 2021 , this specific version addresses critical layout regressions, UI component issues, and downstream compatibility blocks. While it sits chronologically between the feature-dense 2.3.0 framework and the later major overhaul of Bokeh 3.0, version 2.3.3 remains an essential milestone for developers maintaining stable enterprise deployments, particularly those integrated with the Panel dashboarding framework.
Bokeh 2.3.3 is a powerful and versatile data visualization library that can help you unlock the full potential of your data. With its elegant and concise API, Bokeh makes it easy to create stunning visualizations that are both informative and engaging. Whether you're a data scientist, analyst, or developer, Bokeh is definitely worth checking out.
When dealing with large datasets, performance can become a concern. Bokeh provides several strategies to optimize rendering and interactivity.
This comprehensive guide explores Bokeh 2.3.3. We will cover its core philosophy, key features, installation, and practical implementation examples. Why Bokeh 2.3.3 Matters
As documented in the official Bokeh 2.3.3 Release Notes , this patch centers on structural and front-end interface adjustments:
The following production-ready example demonstrates how to leverage the feature set of Bokeh 2.3.3—combining the hatch fill features of the 2.3 line with a highly responsive columnar layout configuration:
save(p, "plot.html") # interactive HTML export_png(p, "plot.png") # requires selenium + phantomjs (older)
While Bokeh 2.3.3 does not introduce new functionality, it is a that resolves real-world issues reported by the community. Users are encouraged to upgrade, especially those running Bokeh in production notebooks or dashboards where interaction reliability is critical.
# dashboard.py from bokeh.plotting import figure, curdoc from bokeh.models import ColumnDataSource, Slider from bokeh.layouts import column, row import numpy as np
Bokeh is an open-source library that allows users to create web-based interactive plots, charts, and dashboards. It's designed to be highly customizable and extensible, making it a favorite among data scientists, researchers, and developers.
A JavaScript library that runs in the browser, translating the Python-defined components into high-performance Canvas or WebGL elements.
After installation, you can verify that Bokeh 2.3.3 is correctly installed by running:
conda install bokeh