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Dash by Plotly VS Bokeh

Compare Dash by Plotly VS Bokeh and see what are their differences

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Dash by Plotly logo Dash by Plotly

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

Bokeh logo Bokeh

Bokeh visualization library, documentation site.
  • Dash by Plotly Landing page
    Landing page //
    2023-05-22
  • Bokeh Landing page
    Landing page //
    2022-11-01

Dash by Plotly features and specs

  • Interactive Visualizations
    Dash by Plotly allows users to create highly interactive visualizations with ease, using a combination of Python, R, or Julia. It supports a wide variety of visualization components, which can be easily customized and stylized to the user's needs.
  • End-to-End Platform
    Dash is an end-to-end platform that covers the entire data visualization pipeline from data processing to the presentation layer. This allows users to seamlessly transition from data analysis to sharing insights without having to switch tools.
  • Open-Source
    Dash is an open-source framework, which allows for a high level of customization. It benefits from community contributions and offers transparency because users can view and modify the source code as needed.
  • Python Integration
    Dash is tightly integrated with Python, which is a major advantage for data scientists and analysts who use Python for data manipulation and analysis. It leverages the robust ecosystem of Python libraries, like Pandas and NumPy.

Possible disadvantages of Dash by Plotly

  • Limited Custom Components
    While Dash provides many components for building applications, it can sometimes be limiting when you need highly customized features or specific integrations that aren't available out of the box.
  • Learning Curve
    For users not familiar with web development concepts (like HTML, CSS, and JavaScript), Dash can have a steep learning curve because it requires understanding how web applications are structured and deployed.
  • Performance
    Dash applications can become sluggish with large datasets or highly interactive charts, as the client-side rendering can be resource-intensive. This can make it difficult to handle applications at scale without optimization.
  • Deployment Complexity
    Deploying Dash applications might be challenging, especially for users without experience in setting up servers or cloud environments. While there are services provided by Plotly for deployment, they can add extra cost and require technical setup.

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.

Analysis of Bokeh

Overall verdict

  • Yes, Bokeh is a good choice for data visualization, particularly if you need to create interactive, high-quality plots that can be shared and displayed on the web.

Why this product is good

  • Bokeh is a powerful and interactive visualization library for Python that is known for its ability to create elegant, scalable, and versatile graphics. It is especially useful for creating web-ready, interactive plots that can be easily embedded into web pages or applications. Bokeh is praised for its intuitive and flexible interface, making it a great choice for both simple and complex visualizations.

Recommended for

  • Data scientists who need to create interactive visualizations for data exploration.
  • Web developers looking to incorporate dynamic plots into their applications.
  • Educators and researchers who need to present data interactively in a web-based format.
  • Anyone seeking a versatile tool compatible with various data formats and capable of producing real-time streaming plots.

Dash by Plotly videos

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Bokeh videos

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"Bokeh" - Netflix Film Review

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Category Popularity

0-100% (relative to Dash by Plotly and Bokeh)

Dash by Plotly

Bokeh

Developer Tools
100 100%
Developer Tools
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Productivity
100 100%
Productivity
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 Dash by Plotly and Bokeh

Dash by Plotly Reviews

Top 10 Tableau Open Source Alternatives: A Comprehensive List
To learn more about Plotly-Dash, you can click here to check out their official website.
Source: hevodata.com

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, Bokeh should be more popular than Dash by Plotly. It has been mentiond 5 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.

Dash by Plotly mentions (1)

  • [Python] NiceGUI: Lassen Sie jeden Browser das Frontend für Ihren Python-Code sein
    Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally,... Source: about 2 years ago

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 Dash by Plotly and Bokeh, you can also consider the following products

Streamlit - Turn python scripts into beautiful ML tools

Plotly - Low-Code Data Apps

Panel - High-level app and dashboarding solution for Python

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

Streamsync - Streamsync is an open-source framework for creating data apps. Build user interfaces using a visual editor; write the backend code in Python.

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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