import bokeh print(bokeh.__version__) # Should output: 2.3.3 Use code with caution. 4. Building Your First Interactive Plot
gridplot() : Builds a strict multi-row, multi-column matrix of synchronized plots. Implementing Built-In Themes
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.
The official Bokeh 2.3.3 release notes highlight several fundamental corrections that address how components adapt to their containing layouts: 1. Layout and Panel Adjustments
Glyphs are the visual building blocks of a Bokeh chart. They represent the geometric shapes used to display data. Common glyphs include: circle() , square() , triangle() (Scatter plots) line() (Time-series and trend lines) vbar() , hbar() (Bar charts) patch() , patches() (Polygons and geographic maps) D. ColumnDataSource (CDS) bokeh 2.3.3
To maximize production efficiency with Bokeh 2.3.3, it helps to understand how it maps to Bokeh’s broader two-tier engine structure:
pip install bokeh==2.3.3
Bokeh at a Glance * Flexible. Bokeh makes it simple to create common plots, but also can handle custom or specialized use-cases. * Bokeh plots Building Charts in Bokeh - Pluralsight
You can organize your visualizations using three primary layout functions: row() : Places plots horizontally. column() : Places plots vertically. import bokeh print(bokeh
pip install --upgrade bokeh
To prove this, the team turned to . This version was particularly exciting for the team because it introduced new capabilities for responsive grid layouts and simplified JSON outputs, making it easier to embed their findings directly into the stadium's executive dashboard.
Offers extensive control over plot aesthetics, including axes, legends, glyphs, and tools. How to Install and Use Bokeh 2.3.3 You can install this specific version using pip or conda . Using pip: pip install bokeh==2.3.3 Use code with caution. Using conda: conda install -c bokeh bokeh=2.3.3 Use code with caution. Example: Basic Scatter Plot in Bokeh 2.3.3
show(p)
It supports streaming data in real-time, making it suitable for live monitoring dashboards.
The JSON file is handed to Bokeh's client-side TypeScript engine ( BokehJS ) running inside the user's browser. This engine renders vector elements natively inside the DOM without requiring a running Python background process.
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 .