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Bokeh VS Plotly

Compare Bokeh VS Plotly and see what are their differences

Bokeh

Bokeh visualization library, documentation site.

Rating
0 reviews
Alternatives Is it good?
Plotly

Low-Code Data Apps

Rating
0 reviews
Alternatives Is it good?
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Which is more popular?

Based on our record, Plotly should be more popular than Bokeh. It has been mentioned 34 times since March 2021.

social mentions
5 vs 34
Charting Libraries popularity
32% vs 68%
alternatives listed
118 vs 240+

Base details

Website, pricing, platforms and company facts side by side.

Bokeh
Plotly
Website docs.bokeh.org plotly.com
Pricing —
Official pricing
Listed in
Charting Libraries Data Visualization Data Dashboard Javascript UI Libraries +1
Application And Data Developer Tools App Development App Deployment Data Visualization Low Code +3

Features and specs

What each product offers, as listed by its team.

Bokeh 5 features
Plotly 6 features
  • 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

  • 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.
  • Interactivity
    Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
  • High-quality visualizations
    It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
  • Versatility
    Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
  • Python integration
    Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
  • Web-based
    The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
  • Open-source
    Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages

  • Performance
    Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
  • Learning curve
    Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
  • Cost for advanced features
    While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
  • Dependency management
    Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
  • Complexity
    For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Analysis

An editorial look at what each product does well and who it suits.

Bokeh
Plotly

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.

Overall verdict

  • Overall, Plotly is a strong choice for those looking to create dynamic and interactive data visualizations, thanks to its range of features and ease of integration with web technologies.

Why this product is good

  • Plotly is considered good because it offers a comprehensive suite of tools for creating interactive visualizations that can be used in web applications, reports, and dashboards. It supports many different types of plots, is easy to use for both beginners and experienced developers, and integrates well with popular programming languages like Python, R, and JavaScript.

Recommended for

    Plotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.

Videos

Walkthroughs and reviews on video.

Bokeh 3 videos + Add
Plotly 5 videos + Add

"Bokeh" - Netflix Film Review

More videos

  • - Bokeh Movie Review
  • - Bokeh - Review

Create Real-time Chart with Javascript | Plotly.js Tutorial

More videos

  • - Introducing plotly.py 3.0
  • - Is Plotly The Better Matplotlib?
  • - Plotly Tutorial 2021
  • - Data Visualization as The First and Last Mile of Data Science Plotly Express and Dash | SciPy 2021

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Bokeh
Plotly
32% 32%
Charting Libraries
68% 68%
18% 18%
Data Visualization
82% 82%
25% 25%
Data Dashboard
75% 75%
36% 36%
Development
64% 64%

User comments

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Bokeh no reviews yet
Plotly no reviews yet

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Bokeh 5 mentions
Plotly 34 mentions
  • [OC] Chemical Diversity of The GlobalChem Common Chemical Universe
    Visualization: https://docs.bokeh.org/en/latest/. Source: over 4 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 / over 4 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... - Source: dev.to / over 4 years ago

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Alternatives to Bokeh and Plotly

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

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