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Panel VS Bokeh

Compare Panel VS Bokeh and see what are their differences

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Panel logo Panel

High-level app and dashboarding solution for Python

Bokeh logo Bokeh

Bokeh visualization library, documentation site.
  • Panel Landing page
    Landing page //
    2023-05-28
  • Bokeh Landing page
    Landing page //
    2022-11-01

Panel features and specs

  • Flexibility
    Panel provides a flexible framework for creating interactive web applications, dashboards, and complex visualizations using Python, allowing developers to leverage their existing Python code without needing to switch to JavaScript or another language.
  • Integration with HoloViz Ecosystem
    Panel integrates seamlessly with other HoloViz tools like HoloViews, GeoViews, and Datashader, enhancing its capabilities for building rich, data-visualization-centric applications.
  • Support for Multiple Backends
    It supports multiple backends, including Bokeh, Plotly, and Matplotlib, giving developers the flexibility to choose their preferred plotting library for rendering their visualizations.
  • Dynamic and Reactive Features
    Panel supports dynamic and reactive UI components that update automatically as data changes, facilitating the creation of interactive and live data applications.
  • Easy Deployment
    Applications built with Panel can be easily deployed on the web using various options, including deploying on Heroku, AWS, or with simple HTTP servers, which helps in transitioning from development to production.

Possible disadvantages of Panel

  • Steep Learning Curve
    For those unfamiliar with the HoloViz ecosystem or Python-based web development, there can be a steep learning curve associated with mastering Panel and its related tools.
  • Performance Limitations
    While Panel is powerful, it may not perform as well as JavaScript-native solutions for extremely high-frequency, real-time data updates due to the overhead of Python-to-JavaScript communication.
  • Limited Community and Resources
    Although growing, the community and resources are not as extensive as some other more-established frameworks like React or Angular, which may lead to a lack of readily available support or third-party plugins.
  • Complexity with Large Applications
    As applications grow in size and complexity, managing state and ensuring efficient communication between components can become challenging.
  • Dependency on Python Environment
    Panel applications require a running Python environment, which can complicate deployment or hosting compared to purely static or client-side applications.

Bokeh features and specs

  • Interactive Visualizations
    Bokeh is designed specifically for creating interactive and highly customizable visualizations, making it suitable for engaging data exploration.
  • Python Integration
    Bokeh integrates well with the Python ecosystem, allowing direct use of pandas, NumPy, and other Python libraries, facilitating seamless data manipulation and visualization.
  • Web Compatibility
    Bokeh generates plots that are ready to be embedded into web applications, making it a powerful tool for creating dashboards and interactive reports.
  • Server Functionality
    Bokeh provides a server component that allows users to build and deploy sophisticated interactive applications using just Python.
  • Variety of Plotting Options
    Bokeh offers a wide range of plotting capabilities including charts, maps, and streamgraphs, enabling users to create complex visual stories.

Possible disadvantages of Bokeh

  • Learning Curve
    Bokeh may have a steeper learning curve for users unfamiliar with JavaScript or those looking for a very simple or quick plotting tool.
  • Performance Issues
    When dealing with very large datasets, Bokeh might suffer from performance issues, as it is primarily client-side rendering.
  • Limited 3D Capabilities
    Bokeh's support for 3D plotting is limited compared to other visualization libraries like Plotly, potentially restricting its use for applications that require 3D visualizations.
  • Documentation and Community Size
    While Bokeh has good documentation, its user community is smaller compared to more mature libraries like Matplotlib, which can mean fewer resources and third-party support options.

Panel videos

+ Add

Ready To Love S7 E8 PANEL REVIEW WITH SPECIAL GUEST #readytolove

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Bokeh videos

+ Add

"Bokeh" - Netflix Film Review

More videos:

Category Popularity

0-100% (relative to Panel and Bokeh)

Panel

Bokeh

Developer Tools
100 100%
Developer Tools
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Web App
100 100%
Web App
0% 0
Data Visualization
0 0%
Data Visualization
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Panel and Bokeh

Panel Reviews

We have no reviews of Panel yet.
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Bokeh Reviews

Top 8 Python Libraries for Data Visualization
Pygal is a Python data visualization library that is made for creating sexy charts! (According to their website!) While Pygal is similar to Plotly or Bokeh in that it creates data visualization charts that can be embedded into web pages and accessed using a web browser, a primary difference is that it can output charts in the form of SVG’s or Scalable Vector Graphics. These...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Panel should be more popular than Bokeh. It has been mentiond 10 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Panel mentions (10)

  • Show HN: Manganite – Quickly turn Jupyter notebooks into web apps
    Manganite allows easy conversion of Jupyter notebooks into dashboards. Simply annotate existing notebooks with Jupyter magics and serve them as interactive web apps. Manganite has been created to empower master and doctoral students in econ and management to turn research notebooks into interactive dashboards. The students use Python for data analysis, math programming, and basic machine learning. Instead of... - Source: Hacker News / over 1 year ago
  • What python library you are using for interactive visualisation?(other than plotly)
    Https://panel.holoviz.org/ It's a web app framework for Python similar to what Dash does for plotly. It plays nicely with bokeh visuals and I think the front-end is built using bokeh css elements. Source: almost 2 years ago
  • How to approach GIS and which language to use
    If you want to build Python dashboards, look at the solara (react-style lib, https://solara.dev/) and panel (https://panel.holoviz.org/). Source: almost 2 years ago
  • Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
    My suggestion is https://panel.holoviz.org/ Fully open sourced, makes it easy to make reactive apps with small changes, can even configured as a graphical REPL. - Source: Hacker News / about 2 years ago
  • Updating a page with MQTT
    I am doing something like this in a [panel](https://panel.holoviz.org/) dashboard, which I am currently converting to nicegui. Maybe I can provide an example in some days. Source: about 2 years ago
View more

Bokeh mentions (5)

  • [OC] Chemical Diversity of The GlobalChem Common Chemical Universe
    Visualization: https://docs.bokeh.org/en/latest/. Source: about 3 years ago
  • Profiling workflows with the Amazon Genomics CLI
    Now that we can get task timing information in a consistent manner, let’s do some plotting. For this, I’m going to use Bokeh which generates nice interactive plots. - Source: dev.to / about 3 years ago
  • 10 Python Libraries For Data Visualization
    Bokeh The Bokeh library is native to Python and is mainly used to create interactive, web-ready plots, which can be easily output as HTML documents, JSON objects, or interactive web applications. Like ggplot, its concepts are also based on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots,... - Source: dev.to / over 3 years ago
  • Graphic library Bokeh is underrated and underdocumented
    It's not in the least bit "underrated" and it's documentation is extensive. Source: almost 4 years ago
  • Help with Bokeh Interactive Plot
    Hi guys! I am currently working on a project to enrich my Master thesis with some interactive plots. I have been using the Bokeh library to make a standalone application, which I was then planning to deploy in Heroku. You can find the code in this repository. But I will also add it at the bottom of the post. Source: about 4 years ago

What are some alternatives?

When comparing Panel and Bokeh, you can also consider the following products

Streamlit - Turn python scripts into beautiful ML tools

Plotly - Low-Code Data Apps

Dash by Plotly - Dash is a Python framework for building analytical web applications. No JavaScript required.

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

Turtle - New kind of anonymous messaging app

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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