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Bokeh 2.3.3 Exclusive -

Bokeh 2.3.3 is a specific version of the Bokeh interactive visualization library released in July 2021. It is a patch-release that primarily addresses bugs related to layouts and extensions. Key Features of Bokeh 2.3.3

Version Focus: As a minor patch release, its main goal was fixing regressions rather than adding major new features.

Fixes: Notable fixes included resolving an issue where the Column layout ignored the scrollable CSS class.

Legacy Context: This version still relied on older WebGL code, which some users found buggy, leading many to later upgrade to version 2.4.x for better performance. Working with Text in Bokeh 2.3.3

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

Axis Labels: You can add line breaks to labels using the \n string.

Text Properties: Visual attributes like text_color, text_font, and text_font_style can be applied to titles, labels, and annotations.

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

Text Glyphs: You can render text directly onto a plot using the text() glyph method, which allows for vectorized text placement. Usage Example (v2.3.3 Syntax)

To display a simple text label on a plot in this version, you would use the following structure:

from bokeh.plotting import figure, show p = figure(width=400, height=400) # Adding text glyphs p.text(x=[1, 2], y=[1, 2], text=["Label A", "Label B"], text_color="firebrick", text_font_size="20pt") show(p) Use code with caution. Copied to clipboard

For more detailed documentation, you can refer to the archived Bokeh 2.3.3 User Guide or see current installation options on PyPI. Styling visual attributes — Bokeh 2.3.3 Documentation

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

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.

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 Bokeh 2

: 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?

Title: "Unlocking Stunning Visualizations with Bokeh 2.3.3: A Comprehensive Guide"

Introduction

Data visualization is an essential aspect of data science, allowing us to communicate complex insights and trends in a clear and concise manner. Among the numerous visualization libraries available, Bokeh stands out for its elegant, concise construction of versatile graphics. In this blog post, we'll dive into the features and capabilities of Bokeh 2.3.3, exploring how you can leverage this powerful library to create stunning visualizations.

What is Bokeh?

Bokeh is an interactive visualization library in Python that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.

Key Features of Bokeh 2.3.3

Bokeh 2.3.3 comes with a wide range of tools and features that make data visualization a breeze. Some of the key features include:

Getting Started with Bokeh 2.3.3

To get started with Bokeh, you'll need to have Python installed on your machine. Then, you can install Bokeh using pip:

pip install bokeh

Here's a simple example to create a line plot using Bokeh: Fixing a critical regression where LabelSet would crash

import numpy as np
from bokeh.plotting import figure, show
# Create a sample dataset
x = np.linspace(0, 4*np.pi, 100)
y = np.sin(x)
# Create a new plot with a title and axis labels
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# Add a line renderer with legend and line thickness
p.line(x, y, legend_label="sin(x)", line_width=2)
# Show the results
show(p)

Advanced Features and Use Cases

Bokeh 2.3.3 is not just limited to simple plots. It's capable of creating complex dashboards and applications. Some advanced features and use cases include:

Conclusion

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.

Resources

By following this guide, you'll be well on your way to creating stunning visualizations with Bokeh 2.3.3. Happy visualizing!

Installation Guide for Bokeh 2.3.3

Installing this specific version is straightforward. It is strongly recommended to use a virtual environment.

Verifying the Installation

import bokeh
print(bokeh.__version__)
# Output: 2.3.3

3. Core Concepts (2.3.3 style)

| Concept | Description | |---------|-------------| | figure() | Creates a new plot with default tools, axes, grids. | | ColumnDataSource | Central data object (like a DataFrame wrapper). | | Glyphs | Visual marks (lines, circles, bars). | | Layout | row, column, gridplot for arranging plots. | | Widgets | Sliders, buttons, dropdowns (from bokeh.models). | | Callback | Python (CustomJS) or server-side callbacks. |

Sample data - simulate stock prices

dates = pd.date_range('2023-01-01', periods=200) prices = 100 + np.cumsum(np.random.randn(200).cumsum()) volume = np.random.randint(1000, 10000, 200)

source = ColumnDataSource(data= 'date': dates, 'price': prices, 'volume': volume, 'moving_avg': pd.Series(prices).rolling(10).mean() )

Line glyphs

p.line('date', 'price', source=source, legend_label="Price", color="navy", alpha=0.7) p.line('date', 'moving_avg', source=source, legend_label="10-day MA", color="firebrick", line_width=2)

Note on JavaScript Dependencies

Bokeh 2.3.3 automatically tries to load BokehJS (the client-side library) from a CDN. If you're working in an air-gapped or offline environment, you can download the BokehJS static files separately and serve them locally.

Bokeh 2.3.3 – Quick Reference & Practical Guide

4. Enhanced HoverTool

The hover tooltip in Bokeh 2.3.3 received patches for: