Bokeh 2.3.3 _top_

Here are a few examples of using Bokeh 2.3.3:

You can verify that the correct version is active by running a quick terminal check: python -c "import bokeh; print(bokeh.__version__)" Use code with caution. Expected output: 2.3.3 4. The Building Blocks of Bokeh 2.3.3

: Resolved an issue where Column layouts ignored the scrollable CSS class and fixed a layout regression specifically affecting Panel dashboards.

output_notebook() # or output_file("plot.html")

, the popular interactive visualization library for Python, continues to solidify its place in the data science ecosystem with version 2.3.3. This release focuses on stability, performance improvements, and critical integration updates with high-performance data handling libraries like Datashader . bokeh 2.3.3

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

. Depending on your context, "full piece" likely refers to one of the following: Bokeh documentation 1. The Bokeh Software Documentation Version 2.3.3 is a stable release of the Bokeh Python library . The "full piece" might refer to the complete source code full documentation for setting up a development environment, which includes: Bokeh documentation Bokeh (Python): The package source code. BokehJS (TypeScript): The client-side library that handles browser rendering. Bokeh documentation 2. Standalone Code Examples In technical forums, "full piece" often refers to a Minimal Reproducible Example (MRE)

When developing text-based elements in this version, you typically use several core models and properties:

In Bokeh 2.3.3, the ColumnDataSource (CDS) is the core object that maps data to your plot elements. It allows multiple glyphs to share the same data and enables interactive features like tooltips. Here are a few examples of using Bokeh 2

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

To get started with Bokeh 2.3.3, you can use the following example code:

While the Bokeh project has since moved to 3.x, certain situations still mandate using the legacy 2.3.3 version: Recommendation

pip install bokeh==2.3.3

from bokeh.layouts import row from bokeh.plotting import figure, show # Create plot 1 p1 = figure(width=300, height=300) p1.circle([1, 2, 3], [4, 5, 6], size=10, color="navy") # Create plot 2 p2 = figure(width=300, height=300) p2.square([1, 2, 3], [6, 5, 4], size=10, color="firebrick") # Put the plots in a row layout layout = row(p1, p2) show(layout) Use code with caution. Best Practices for Bokeh 2.3.3

Fixed a bug where plot heights could not be reduced below 600px.

It creates complex, server-backed interactive dashboards.