Bokeh 2.3.3 Guide
In the summer of 2021, as the world began to open up, a small data analytics team was tasked with a sensitive project: quantifying the "enthusiasm" of the return to live sports. The hypothesis was that after a year of silence, the crowds would be louder than ever.
To prove this, the team turned to Bokeh 2.3.3. 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.
from bokeh.layouts import column from bokeh.models import Slider, CustomJS, ColumnDataSource from bokeh.plotting import figure, showx = [1,2,3,4,5] y = [2,4,6,8,10] source = ColumnDataSource(data=dict(x=x, y=y))
p = figure() p.line("x", "y", source=source)
slider = Slider(start=0, end=10, step=1, value=1, title="Multiplier")
callback = CustomJS(args=dict(source=source, slider=slider), code=""" const data = source.data; const multiplier = slider.value; const new_y = data['x'].map(x => x * multiplier); data['y'] = new_y; source.change.emit(); """)
slider.js_on_change('value', callback)
show(column(p, slider))
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 bokeh 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:
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.
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= In the summer of 2021, as the world
AnacondaでOpen CVのインストールに失敗 #Python - Qiita
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.
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 — While Bokeh 2
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:
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!
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.