Bokeh 2.3.3 _hot_

Bokeh 2.3.3, released in July 2021, is a focused patch-release of the Bokeh interactive visualization library. While it isn't a major feature update, it serves as a critical maintenance release that addresses specific layout regressions and extension bugs identified in the broader 2.3.x series. Performance and Stability

This version is primarily valued for its stability improvements. It resolved several frustrating UI issues, such as:

Layout Fixes: Corrected issues where column CSS classes like scrollable were ignored and fixed layout regressions in the panel component.

Axis Formatting: Addressed bad formatting of y-axis labels when using specific themes.

Widget Behavior: Fixed a bug where dropdown menus were hidden in multi-choice widgets and ensured the active tab stays in view upon rendering. Key Features of the 2.3 Series

For users looking at this specific version, it carries the significant advancements of the 2.3 major release, including:

Improved WebGL support: Better performance for large datasets.

Enhanced Documentation: A restructured User Guide and expanded documentation to help new users get started.

Server Capabilities: Robust tools for building sophisticated web applications and dashboards without needing to write JavaScript. Community Perspectives

Users often compare Bokeh's flexibility favorably against other frameworks for specific use cases:

“I actually loved using Bokeh... I like attaching callbacks to the object that started the callback... I also like not having to worry about saving/loading the current state of the application.” Bokeh Discourse · 5 years ago

“Bokeh's architecture is more suited for complex layouts and interactive elements than Matplotlib, making it a top choice for dashboards.” StrataScratch · 1 year ago

Verdict: Bokeh 2.3.3 is a reliable "legacy" choice if you are maintaining a project from the 2021 era. However, for new projects, you should consider the latest version (currently 3.x) which offers modern contour plots and significantly restructured APIs.

Bokeh 2.3.3 is a highly stable patch release of the Bokeh interactive visualization library that resolves crucial layout regressions and extension bugs to improve the reliability of Python-based web dashboards.

Released in July 2021, Bokeh 2.3.3 represents a vital maintenance milestone in the 2.x lifecycle of the Bokeh data visualization ecosystem. This release continues to be widely used in enterprise legacy systems, specific LTS Python environments, and production pipelines where stability and backwards compatibility are absolute priorities. 🛠️ The Purpose of Bokeh 2.3.3

As a maintenance patch, Bokeh 2.3.3 does not introduce new visual glyphs or sweeping architectural changes. Instead, it serves as a critical stabilization release. By addressing several front-end layout issues, server rendering problems, and JavaScript-to-Python model synchronization errors, this version prevents visual regressions in complex analytical dashboards.

Python developers utilize Bokeh to build high-performance, interactive visualizations directly for modern web browsers without needing to write client-side JavaScript. Version 2.3.3 secures this workflow by ensuring that the browser-based client (BokehJS) interprets Python commands predictably and uniformly. 📈 Key Bug Fixes & Improvements

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

Scrollable Columns: Fixed an issue where the Column layout model ignored the scrollable CSS class, preventing the correct behavior of long lists and overflow UI elements.

Panel Regression: Patched a regression affecting downstream dashboard frameworks like Panel, ensuring seamless integration and layout rendering for advanced multi-page data applications.

Div Model Interoperability: Corrected specific styling differences in the Div model, preventing unwanted CSS shifts between different views or parent containers.

Minimum Plot Height: Fixed an explicit bug that prevented plot heights from dropping below 600px. Developers regained the flexibility to customize compact visualizations for mobile views or compressed grids. 2. UI and Widget Enhancements

Active Tab Viewability: Ensured that the active tab in a layout component is forced directly into view when rendering. This creates a smoother initial load state for multi-tab analytical interfaces.

Multi-Choice Dropdowns: Fixed an issue where dropdown menus in the MultiChoice input widget were erroneously hidden or cut off by parent overflow rules. 3. Build & Sub-Resource Integrity

Theme Label Formatting: Addressed a formatting issue with y-axis labels when applying custom styles or themes.

CDN Extension Management: Configured custom extensions to fetch the exact matching version directly from the Bokeh CDN. This prevents major security and compatibility issues resulting from mismatched server and client environments. 💻 Sample Code: Creating a Basic Plot in Bokeh 2.3.3

Creating a scatter plot with panning, zooming, and hover tools is straightforward in Bokeh 2.3.3. Below is a complete standalone example utilizing the bokeh.plotting interface:

from bokeh.plotting import figure, output_file, show from bokeh.models import HoverTool # Step 1: Configure output to a standalone HTML file output_file("bokeh_233_demo.html") # Step 2: Initialize your figure with specific dimensions and tools p = figure( title="Bokeh 2.3.3 Maintenance Release Demo", x_axis_label="X Axis", y_axis_label="Y Axis", plot_width=700, # Below the 600px restriction bug fixed in 2.3.3 plot_height=450, tools="pan,box_zoom,reset,save" ) # Step 3: Populate sample data x_data = [1, 2, 3, 4, 5] y_data = [6, 7, 2, 4, 5] # Step 4: Render your visual elements (glyphs) p.circle(x_data, y_data, size=15, color="navy", alpha=0.6) # Step 5: Inject custom interactivity hover = HoverTool(tooltips=[("Value (X, Y)", "(@x, @y)")]) p.add_tools(hover) # Step 6: Generate the visualization show(p) Use code with caution. ⚖️ When to Use Bokeh 2.3.3 Today

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

If your system relies on Python 3.6 or early Python 3.7 configurations, Bokeh 2.3.3 provides a compatible and reliable backend. Downstream Compatibility

Legacy versions of analytics packages like HoloViews or older iterations of Panel rely heavily on the DOM and layout architecture of Bokeh 2.x. Air-gapped Environments

For older enterprise architectures that cache specific Sub-Resource Integrity (SRI) hashes, Bokeh 2.3.3 supplies vetted script hashes for stable deployment.

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:

  • Interactive Plots: Bokeh plots are interactive by default, allowing users to hover over data points, zoom in and out, and pan across the plot area.
  • Multiple Rendering Options: Bokeh supports rendering plots in various formats, including HTML, PNG, and PDF.
  • Customizable: Bokeh offers extensive options for customizing the appearance of plots, including colors, fonts, and layouts.
  • Cross-Platform Compatibility: Bokeh works seamlessly across different platforms and devices.

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: bokeh 2.3.3

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:

  • Dashboards: Bokeh makes it easy to create dashboards by combining multiple plots and widgets into a single, interactive application.
  • Custom Widgets: Bokeh provides a range of pre-built widgets, such as sliders, dropdown menus, and text inputs, which can be used to create interactive applications.
  • Real-time Data: Bokeh supports streaming data, allowing you to create real-time visualizations that update as new data arrives.

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

  • Bokeh Documentation: The official Bokeh documentation provides an exhaustive guide to getting started with Bokeh, including tutorials, examples, and API references.
  • Bokeh GitHub Repository: The Bokeh GitHub repository contains the source code for Bokeh, as well as examples and issue tracker.

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

Bokeh 2.3.3, released in July 2021, was a critical patch release that prioritized stability and visual consistency within the Bokeh interactive visualization library. While minor versions like 2.3 introduced heavy-hitting features like multi-line axis labels and improved log-axis rendering, the 2.3.3 update focused on refining the user experience through precise layout and extension fixes. The Role of Patch Stability

In the software lifecycle, patch releases like 2.3.3 serve as the "finishing sander" for broader feature sets. Developers using Bokeh for high-stakes data dashboards require the library to behave predictably across various browser environments and CSS configurations. Version 2.3.3 addressed several "edge case" bugs that previously caused visual regressions or functional hiccups in complex layouts. Key Technical Improvements

The release notes for 2.3.3 highlight a series of targeted fixes that improved both the aesthetic and functional quality of Bokeh plots:

Layout & CSS Consistency: Fixed issues where the scrollable CSS class was ignored by column models and resolved layout regressions specifically affecting the Panel integration.

Visual Formatting: Addressed a bug that caused incorrect formatting of y-axis labels when specific themes were applied, ensuring that branded or custom-styled plots remained legible.

UI Component Reliability: Improved the MultiChoice widget by fixing a bug where dropdown menus were hidden, and ensured that active tabs were correctly brought into view upon rendering.

Extension Infrastructure: Mandated that extensions fetch exact versions from the Content Delivery Network (CDN), a change designed to prevent version mismatches that could break custom user-built components. Documentation and Community Support

Beyond code fixes, the 2.3.3 release coincided with an expansion of the Bokeh Documentation. The maintainers utilized this window to clarify existing guides, helping users better navigate complex topics like Bokeh Server deployment and custom JavaScript extensions. Conclusion

While Bokeh has since moved into the 3.x branch—which introduced major redesigns to CSS interoperability and removed support for legacy browsers like IE—version 2.3.3 remains a representative milestone of the 2.x era. It exemplified the project's commitment to incremental improvement, ensuring that the powerful data-linking and interactive capabilities introduced in the 2.3 series were reliable enough for production-level data science workflows.

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:

  1. Improved Performance: Bokeh 2.3.3 includes various performance optimizations, resulting in faster rendering and interaction with plots.
  2. Enhanced Hover Tool: The HoverTool has been improved to support more advanced hover-over text formatting and customization.
  3. New Layout Features: Bokeh 2.3.3 introduces new layout features, such as the ability to create complex, nested layouts using the column and row methods.
  4. Better Support for Large Datasets: Bokeh 2.3.3 includes improvements to handling large datasets, including more efficient data streaming and buffering.
  5. Updated Documentation: The Bokeh documentation has been updated to include more examples, tutorials, and reference materials.

What's New in Bokeh 2.3.3

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

  • New Features:
    • Added support for streaming data with the stream method.
    • Introduced the BokehJS API for custom JavaScript extensions.
  • Improvements:
    • Enhanced performance for large datasets.
    • Improved hover-over text customization.
    • Better support for nested layouts.
  • Bug Fixes:
    • Fixed issues with axis formatting and tick labels.
    • Resolved problems with saving and loading plots.

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.

Bokeh 2.3.3 is a specific maintenance release of the popular Python library used for creating interactive, web-ready visualizations. Released in mid-2021, this version focused on stabilizing the significant architectural improvements introduced in the 2.x series. Overview of Bokeh 2.3.3

The primary purpose of Bokeh is to bridge the gap between powerful Python data analysis and the interactive capabilities of modern web browsers. Unlike static plotting libraries, Bokeh produces JSON objects that are rendered by BokehJS (a JavaScript library), allowing users to interact with data through zooms, pans, and hover tools without needing to write JavaScript themselves. Key Features and Capabilities

Interactive Graphics: Bokeh 2.3.3 excels at creating complex, high-performance dashboards and plots that handle large or streaming datasets.

Versatile Output: It supports multiple output formats, including standalone HTML files, server-side applications via the Bokeh Server, and integration within Jupyter Notebooks.

Customization: The library provides low-level control over every visual element, from glyph properties to layout positioning, while also offering high-level "charts" for quick data exploration.

Integration: It works seamlessly with the broader PyData stack, including Pandas dataframes and NumPy arrays. The Role of Version 2.3.3

In the software lifecycle, version 2.3.3 served as a critical patch and refinement release. It addressed minor regressions and bugs found in previous 2.3 sub-versions, ensuring compatibility with evolving dependencies like Tornado and Jinja2. For developers at the time, it represented a stable environment for production-level dashboards before the eventual transition to the 3.0 release branch. Conclusion

Bokeh 2.3.3 remains a testament to the library's commitment to providing data scientists with a professional tool for communication. By simplifying the creation of "D3-style" visualizations using only Python, it democratizes high-end web design for the scientific community.

Bokeh 2.3.3: Enhancing Performance and Datashader Integration

Bokeh, 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

This article highlights what makes Bokeh 2.3.3 a reliable choice for creating complex, interactive web-based visualizations. Key Updates in Bokeh 2.3.3

Improved Datashader Compatibility: A major highlight of this version is the improved interaction with HoloViews and Datashader. Users working with large datasets can now leverage spread and rasterize operations with better colorbar and hover tool support, particularly noted in the Datashader 0.13 release.

Stability and Reliability: 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.

Widespread Ecosystem Support: Bokeh 2.3.3 is widely available across major package managers, including Anaconda, ensuring seamless integration into existing data science stacks. Why Choose Bokeh 2.3.3?

Handles Large Data: When paired with Datashader, Bokeh 2.3.3 effectively renders millions or billions of points, bypassing browser rendering limitations.

Interactive Dashboards: It creates complex, server-backed interactive dashboards.

Python Native: It allows creating interactive plots without needing extensive JavaScript knowledge. Example Integration (Bokeh 2.3.3 & Datashader)

Users can now create more complex visualizations, such as sparse scatterplots on large datasets, using datashader and holoviews.

import dask.dataframe as dd import holoviews as hv from holoviews.operation.datashader import rasterize, dynspread import bokeh hv.extension("bokeh") # Example for rendering large datasets # df = dd.read_parquet('your_data.parq').compute() # pts = hv.Points(df, ['x_col', 'y_col']) # plot = dynspread(rasterize(pts)).opts(cnorm='log', colorbar=True) Use code with caution. Copied to clipboard Conclusion

Bokeh 2.3.3 serves as a refined, reliable version that empowers data scientists to create interactive, large-scale visualizations, particularly when working within the HoloViz ecosystem. It is an excellent choice for projects requiring interactive plots with high-performance, large-data capability. g., a map, a large scatter plot)? Compare Bokeh 2.3.3 to a newer version (like Bokeh 3.x)?

Create a tutorial for setting up this version in a virtual environment? Datashader 0.13 Release - HoloViz Blog - HoloViews

E.g. in holoviews, use spread(rasterize(obj)).opts(cnorm='eq_hist', cmap='fire') (or cnorm='log' ) instead of datashade(obj, cmap=

AnacondaでOpen CVのインストールに失敗 #Python - Qiita

The story of Bokeh 2.3.3 is one of meticulous refinement rather than flashy new features. Released in

, this specific version served as a critical "patch-release," acting as the final polish for the significant 2.3 series before the library moved toward more major structural changes in later versions like 3.0 [3, 20]. The Role of Bokeh 2.3.3 While version 2.3 introduced heavy hitters like multi-line axis labels and improved log-axis rendering

, 2.3.3 was the "cleanup crew" [4, 3]. It focused on fixing persistent bugs that hindered the user experience, particularly in complex layouts: The Layout Fixer

: It resolved a frustrating bug where columns would ignore CSS "scrollable" classes, making it easier for developers to build dense, interactive dashboards [3]. Visual Consistency : It corrected issues where

-axis labels were poorly formatted when using specific themes, ensuring that professional-grade charts didn't lose their aesthetic appeal [3]. Widget Reliability

: It fixed a bug where dropdown menus were being hidden in "multi-choice" widgets, a small but vital fix for user interface (UI) functionality [3]. Context within the Bokeh Timeline

In the broader "story" of this Python library, 2.3.3 represented the peak of the 2.x era's stability. Soon after, Bokeh 2.4 would introduce math text support (LaTeX) and WebGL improvements, eventually leading to the massive 3.0 release that dropped support for legacy browsers like Internet Explorer to embrace modern web standards [5, 17, 18, 20].

For developers, 2.3.3 was the version you updated to when you wanted your existing 2.x projects to be as bug-free and smooth as possible before considering a major migration. You can still explore the original 2.3.3 documentation or see the full release notes on GitHub

to see every specific fix that made it into this milestone [2, 37]. Do you have a specific bug from that era you're trying to troubleshoot or recreate?

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

  • Interactive Visualizations: Bokeh allows you to create interactive plots that can be zoomed, panned, and hovered over to reveal additional information.
  • Web-based: Bokeh plots can be easily embedded in web applications, making it a great choice for data scientists and web developers.
  • High-level Interface: Bokeh provides a high-level interface for creating plots, making it easy to get started and create complex visualizations quickly.

New Features in Bokeh 2.3.3

  • Improved Performance: Bokeh 2.3.3 includes several performance improvements, making it faster and more efficient.
  • Enhanced Hover Tool: The hover tool has been enhanced to support additional features, such as displaying multiple lines of text and showing images.
  • New Glyphs: Bokeh 2.3.3 includes several new glyphs, including a text glyph for displaying text on a plot.

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?

Verifying the Installation

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

Bokeh 2.3.3: A Deep Dive into the Stable Workhorse of Interactive Data Visualization

In the fast-evolving world of data science, it's easy to get caught up in the latest releases, beta features, and breaking changes. However, seasoned developers and data engineers know the immense value of a stable, well-tested release. Enter Bokeh 2.3.3—a version that, while not the absolute newest, represents a golden standard for reliability, performance, and production-ready interactive visualization.

Released as a patch update to the popular 2.3 series, Bokeh 2.3.3 consolidates months of bug fixes and minor enhancements without introducing the architectural shifts found in later versions (like the Bokeh 3.0 line). For teams maintaining legacy dashboards, educational platforms, or large-scale data applications, this version is the unsung hero. This article explores everything you need to know about Bokeh 2.3.3: its key features, why you might choose it over newer releases, how to install it, and practical examples to get you started. Interactive Plots : Bokeh plots are interactive by

7. Bokeh Server Application (app.py)

# Run with: bokeh serve app.py
from bokeh.io import curdoc
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource
from bokeh.layouts import column
from random import random

source = ColumnDataSource(dict(x=[0], y=[0]))
p = figure()
p.circle(x="x", y="y", source=source)


def update():
new_data = dict(x=[source.data["x"][-1] + 1],
y=[random()])
source.stream(new_data, rollover=20)


curdoc().add_root(column(p))
curdoc().add_periodic_callback(update, 1000)

Closing Notes

While Bokeh 2.3.3 does not introduce new functionality, it is a stability-focused release 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.

For a complete list of closed issues and pull requests, refer to the Bokeh 2.3.3 GitHub milestone.

This write-up is based on the official Bokeh changelog and community feedback following the 2.3.3 release.

Bokeh 2.3.3 is a specific patch release of the Bokeh interactive visualization library for Python, launched in July 2021. While it is now considered an older version—with current releases being in the 3.x series—it remains relevant for legacy projects or systems constrained by older Python environments (like Python 3.6). 🚀 Key Fixes in Version 2.3.3

This version primarily addressed layout regressions and minor bugs rather than adding major new features. Notable fixes included:

Layout Adjustments: Resolved issues where columns would ignore scrollable CSS classes and fixed layout differences in Div models.

Panel Regressions: Fixed a regression that affected how panels were rendered within layouts.

Axis Formatting: Corrected a bug where y-axis labels were formatted incorrectly when using custom themes.

Multi-Choice Widgets: Fixed a bug that caused dropdown menus to be hidden in certain multi-choice scenarios.

CDN Extensions: Updated how extensions fetch versions from the CDN to ensure exact version matching. 🛠️ Critical Resources for 2.3.3

If you are currently working with this version, the following resources are essential:

User Guide: The Bokeh 2.3.3 User Guide provides a comprehensive look at creating layouts, handling categorical data, and mapping geo data.

Reference Gallery: You can view server app examples such as interactive weather statistics and Gapminder-style demos specific to this version's API.

CDN Links: For manual embedding, the core JS files are available via the official Bokeh CDN:

The phrase " bokeh 2.3.3 " refers to a specific version of the interactive visualization library, released in

. 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)

—a complete, self-contained script used to demonstrate a feature or bug. For example, version 2.3.3 users often share "full pieces" of code to troubleshoot layout regressions in the model or panels. Bokeh documentation 3. Misleading "Apk" or Video Content

You may encounter searches or TikTok videos mentioning "Download Bokeh 2.3.3 Apk" or "full piece" videos. Be cautious: Getting Set Up — Bokeh 2.3.3 Documentation 2 Jun 2020 —

Unlocking Stunning Visuals: A Comprehensive Guide to Bokeh 2.3.3

Bokeh is a popular Python library used for creating interactive visualizations and dashboards. With its latest release, Bokeh 2.3.3, users can now enjoy a wide range of features and improvements that make data visualization even more powerful and intuitive. In this article, we'll explore the key features, enhancements, and use cases of Bokeh 2.3.3, providing you with a comprehensive guide to unlocking stunning visuals.

What is Bokeh?

Bokeh is an open-source Python library designed to help data scientists and developers create interactive visualizations and dashboards. It provides a high-level interface for drawing plots, charts, and other graphical elements, making it easy to create web-based interactive plots. Bokeh's primary goal is to provide a simple and elegant way to create interactive visualizations that can be easily shared and deployed.

Key Features of Bokeh 2.3.3

Bokeh 2.3.3 comes with a range of exciting features and improvements. Some of the key highlights include:

  • Improved Performance: Bokeh 2.3.3 brings significant performance enhancements, making it possible to render large datasets with ease. This is particularly useful for users working with big data.
  • Enhanced Hover Tool: The HoverTool in Bokeh has been revamped to provide more flexibility and customization options. This allows users to create more informative and interactive visualizations.
  • New Color Palette: Bokeh 2.3.3 introduces a new color palette that provides a wider range of colors and improved colorblind friendliness. This makes it easier to create visually appealing visualizations.
  • Support for Latest Python Versions: Bokeh 2.3.3 supports the latest versions of Python, including Python 3.9 and 3.10.

New Features in Bokeh 2.3.3

In addition to the key features mentioned above, Bokeh 2.3.3 also introduces several new features, including:

  • StreamPlot: Bokeh 2.3.3 introduces a new StreamPlot feature that allows users to create animated plots. This is particularly useful for visualizing time-series data.
  • ** Multi-Canvas Support**: Bokeh 2.3.3 now supports multiple canvases, making it possible to create complex, multi-plot visualizations.

Enhancements and Bug Fixes

Bokeh 2.3.3 also includes several enhancements and bug fixes. Some of the notable enhancements include:

  • Improved Documentation: The Bokeh documentation has been improved to provide more comprehensive and easy-to-use guides.
  • Better Handling of Large Datasets: Bokeh 2.3.3 includes improved handling of large datasets, making it possible to render complex visualizations with ease.

Use Cases for Bokeh 2.3.3

Bokeh 2.3.3 can be used in a variety of scenarios, including:

  • Data Exploration: Bokeh 2.3.3 is ideal for data exploration and visualization. Its interactive features make it easy to explore and understand complex data.
  • Dashboarding: Bokeh 2.3.3 can be used to create stunning dashboards that provide real-time insights into data.
  • Reporting: Bokeh 2.3.3 can be used to create interactive reports that provide detailed insights into data.

Example Code: Getting Started with Bokeh 2.3.3

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

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 new plot
p = figure(title="simple line example", x_axis_label='x', y_axis_label='y')
# Add a line to the plot
p.line(x, y, legend_label="sin(x)", line_width=2)
# Show the results
show(p)

This code creates a simple line plot using Bokeh 2.3.3.

Conclusion

Bokeh 2.3.3 is a powerful and feature-rich library for creating interactive visualizations and dashboards. With its improved performance, enhanced HoverTool, and new color palette, Bokeh 2.3.3 provides a comprehensive platform for data scientists and developers to create stunning visuals. Whether you're working with big data, creating dashboards, or simply exploring data, Bokeh 2.3.3 is an ideal choice. Try it out today and unlock the full potential of your data!

4. Documentation & Examples

  • API Reference: Fixed dozens of broken cross-references in the API documentation related to the bokeh.models module.
  • Example Scripts: Updated all example notebooks to use the correct import paths for output_notebook and show, which were outdated in the previous packaging.

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.

1. "RuntimeError: Models must be owned by only a single document"

This occurs when you reuse the same figure or ColumnDataSource in two different layouts. Fix: Create a new source for each independent document, or use bokeh.io.curdoc() to manage ownership properly (for server apps).

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. |