Des questions ? (+33) 04 73 86 60 63

Bokeh 2.3.3 Access

| Problem | Fix | |---------|-----| | Jupyter plot not showing | Use output_notebook() before show(). | | HoverTool shows ??? | Ensure tooltips use field names matching ColumnDataSource columns. | | Layout breaks | Use sizing_mode="stretch_width" or "stretch_both" on top-level layout. | | Widgets not updating | Check that CustomJS uses source.change.emit(). |

output_file("bokeh233_stock_demo.html") show(layout)

Run this script:

python stock_viewer.py

You will see a fully interactive HTML document open in your browser. Notice how the hover tool works reliably, the table updates smoothly, and the WebGL backend (if you had thousands more points) would handle it gracefully—all thanks to the refinements of Bokeh 2.3.3.

In a world chasing the newest features, Bokeh 2.3.3 stands as a testament to the value of stability. It offers a mature, bug-free interactive visualization engine that has been battle-tested in thousands of production dashboards, financial applications, and scientific research tools. For anyone maintaining systems that rely on the Bokeh 2.x API, this version is the definitive upgrade—the final polished gem before the paradigm shift of Bokeh 3.0.

Whether you're a data engineer preserving a legacy dashboard, a professor teaching a course with fixed dependencies, or a developer who values "if it ain't broke, don't fix it," Bokeh 2.3.3 delivers. Install it, explore its capabilities, and appreciate the craftsmanship of a library that turned complex web visualizations into elegant Python code.

Have you used Bokeh 2.3.3 in production? Share your experience or migration story in the comments below.

The version Bokeh 2.3.3 was a minor patch release in the Bokeh 2.3 series, issued on May 10, 2021.

Here is the story and context around that release:

The Backstory:
Bokeh 2.3.0 had introduced major new features, including the highly anticipated bokeh.models.graphs.GraphRenderer improvements and better support for MultiLine and Patches with holes. However, like any major release, it came with bugs.

What happened in 2.3.3?
This release focused entirely on stability and bug fixes. No new features were added. Key fixes included:

Why does it matter?
For users at the time, Bokeh 2.3.3 was the recommended stable version to upgrade to if they had experienced issues with 2.3.0, 2.3.1, or 2.3.2. It represented the Bokeh team’s commitment to rapid bug-fixing after a feature release.

Where it fits in Bokeh history:
Bokeh 2.3.x was the last major series before Bokeh 2.4.0 (released November 2021), which added significant features like multi_handles for legends and new DataTable sorting. After that, Bokeh 3.0 (late 2022) dropped Python 2.7 and 3.6 support and introduced major breaking changes (e.g., removing bokeh.charts and old-style callbacks).

So today, Bokeh 2.3.3 is mostly of historical interest — unless you're maintaining a legacy project pinned to Python 3.6 or using an environment that cannot upgrade to Bokeh 3.x due to API changes.

Bokeh 2.3.3 , you are working with a legacy release of the powerful Python interactive visualization library. While newer versions exist, 2.3.3 is often maintained for specific dependency environments. 🛠️ Official Documentation & Installation The most authoritative "deep guide" is the archived Bokeh 2.3.3 User Guide

, which covers everything from basic glyphs to server deployment. Bokeh documentation Installation : Use pip to target this specific version: pip install bokeh==2.3.3 Sample Data bokeh 2.3.3

: To follow many tutorial examples, you may need to download the companion datasets using: bokeh sampledata Bokeh documentation 💡 Core Architecture: How 2.3.3 Works Bokeh operates on a two-part system: Python Library

: You define your plots, widgets, and logic in Python. It generates a declarative JSON object graph.

: A client-side JavaScript library that takes that JSON and renders the actual interactive visualization in the browser. Bokeh documentation 🗝️ Key Features in 2.3.3

Version 2.3.3 introduced or refined several critical features that were major updates at the time: Hatch Patterns

: Support for textured fills (dots, stripes, etc.) on all fill-able glyphs and annotations. Multi-line Labels : The ability to have multi-line axis and tick labels. WebGL Acceleration

: Enhanced performance for large datasets (thousands of points) by offloading rendering to the GPU. SVG Export

: While PNG was standard, 2.3.3 documentation provides specific paths for exporting layouts as SVGs Bokeh documentation 🏗️ Building a Basic Plot In Bokeh 2.3.3, the bokeh.plotting interface is the most common entry point: # Create a figure with specific tools = figure(title= Basic Line Plot , x_axis_label= , y_axis_label= pan,wheel_zoom,box_zoom,reset # Add a renderer ], line_width= # Display the result Use code with caution. Copied to clipboard 🚀 Advanced Usage Bokeh Server

: Use this to build applications where Python code reacts to browser events (like sliders or selections). You run these apps via the bokeh serve Interactions CustomJS callbacks

for interactions that don't require a Python server, allowing your plots to remain interactive even as standalone HTML files. Integration : Seamlessly works with Jupyter Notebooks by calling output_notebook() Bokeh documentation ⚠️ Version-Specific Warnings Python Compatibility

: This version is typically used with Python 3.6 through 3.9. Check your environment if you encounter installation errors. Documentation Warning : Ensure you are looking at the /en/2.3.3/ path in the docs; the

documentation contains breaking changes and new syntax (like the bokeh.models overhaul) not present in 2.3.3. Bokeh documentation specific code example

(like a real-time dashboard or a complex map) to build with Bokeh 2.3.3? User guide — Bokeh 2.3.3 Documentation

Bokeh 2.3.3: A Powerful Data Visualization Library

Bokeh is a popular Python library used for creating interactive and web-based visualizations. The latest version, Bokeh 2.3.3, offers a wide range of tools and features that make it easy to create stunning plots and dashboards. In this write-up, we'll explore the key features and improvements in Bokeh 2.3.3.

Key Features

New Features in Bokeh 2.3.3

Example Use Case

Here's an example of how to create a simple line plot using Bokeh 2.3.3:

import numpy as np
from bokeh.plotting import figure, show
# Create some data
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
# Create a figure
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# Add a line glyph
p.line(x, y, legend_label="sin(x)", line_width=2)
# Show the plot
show(p)

This code creates a simple line plot of the sine function.

Conclusion

Bokeh 2.3.3 is a powerful data visualization library that offers a wide range of tools and features for creating interactive and web-based visualizations. With its high-level interface and extensive customization options, Bokeh is an excellent choice for data scientists and web developers. Whether you're creating simple plots or complex dashboards, Bokeh 2.3.3 has got you covered.

Depending on whether you're talking about the data visualization library or the photography technique, here are two ways to frame your post: Option 1: For Developers (The Python Library) If you're highlighting Bokeh 2.3.3

, it's often cited as a "gold standard" for stability. Many developers prefer staying on this version to avoid rendering issues memory leaks found in some later 2.4.x releases. Post Idea: "Still rocking Bokeh 2.3.3

for my production dashboards! 📊 Sometimes the latest version isn't always the greatest when it comes to stability. If you're running into export_png

timeouts or layout shifts in newer builds, rolling back to 2.3.3 might just be the fix you need. [Source: Bokeh Discourse ] #DataViz #Python #BokehJS" Option 2: For Photographers (The Aesthetic)

If you're using "2.3.3" as a creative tag (perhaps a 2-meter distance, f/3.3 aperture, or similar), focus on the quality of the blur Post Idea:

"Chasing that perfect creamy background. ✨ To get the best bokeh, remember the golden rule: maximize distance between your subject and the backdrop. 📸 [Source: Canon Europe Quick tips for this look: Wide Aperture : Stick to f/2.8 or lower if your lens allows it. Long Focal Length

: 85mm or 135mm lenses compress the background beautifully. [Source: Pocket Creatives Light Orbs

: Look for 'point' light sources like fairy lights or street lamps for those iconic circles. [Source: of a data visualization project?

Overview of Bokeh

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.

Key Features of Bokeh 2.3.3

The Bokeh 2.3.3 release includes several new features, improvements, and bug fixes. Some of the key highlights include:

What's New in Bokeh 2.3.3

Here's a summary of the major changes in Bokeh 2.3.3:

  • Improvements:
  • Bug Fixes:

    • Upgrade and Installation

    To upgrade to Bokeh 2.3.3, you can use pip:

    pip install --upgrade bokeh

    Or, if you're using conda:

    conda update bokeh

    If you're new to Bokeh, you can install it using pip or conda:

    pip install bokeh

    or

    conda install bokeh

    Example Use Cases

    Here are a few examples of using Bokeh 2.3.3:

    Example 1: Simple Line Plot

    import numpy as np
from bokeh.plotting import figure, show
x = np.linspace(0, 10, 100)
y = np.sin(x)
p = figure(title="simple line plot")
p.line(x, y, legend_label="sin(x)")
show(p)

    Example 2: Interactive Dashboard

    import numpy as np
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, HoverTool
# Create a sample dataset
x = np.random.normal(size=100)
y = np.random.normal(size=100)
# Create a ColumnDataSource
source = ColumnDataSource(data=dict(x=x, y=y))
# Create a figure
p = figure(title="Interactive Dashboard")
# Add a line renderer
p.line('x', 'y', source=source)
# Add a hover tool
hover = HoverTool(tooltips=[
    ("x", "@x"),
    ("y", "@y"),
])
p.add_tools(hover)
# Show the plot
show(p)

    These examples demonstrate the simplicity and flexibility of Bokeh 2.3.3. With its powerful features and extensive customization options, Bokeh is an ideal choice for creating interactive visualizations and dashboards. | Problem | Fix | |---------|-----| | Jupyter

    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.