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Bokeh vs D3.js: What are the differences?


In this article, we will discuss the key differences between Bokeh and D3.js, two popular JavaScript libraries used for data visualization. These libraries provide powerful tools and functionalities for creating interactive visualizations on websites.

  1. Programming Paradigm: Bokeh is primarily a Python library that generates interactive visualizations in JavaScript. It provides a high-level API and follows a declarative approach, allowing users to create visualizations by specifying their properties and interactions in Python code. On the other hand, D3.js is a JavaScript library that focuses on manipulating the Document Object Model (DOM) directly using a functional and event-driven approach. It provides a low-level API, allowing users to have fine-grained control over every aspect of their visualizations.

  2. Data Binding and Manipulation: Bokeh provides a higher level of abstraction for data binding and manipulation. It has built-in tools to handle common tasks such as data transformation, aggregation, and filtering. D3.js, on the other hand, has a lower level of abstraction and requires users to manually manipulate the data using JavaScript. It provides powerful data manipulation capabilities, but requires more code and effort from the user's side.

  3. Interactivity and Animation: Bokeh provides an extensive set of tools and widgets for creating interactive visualizations. It supports various types of interactivity, such as linked panning and brushing, hover tooltips, and interactive selection. Bokeh also has built-in support for animations and transitions. D3.js, being a more low-level library, provides more flexibility in terms of interactivity and animation. Users can create custom interactions and animations using the full power of JavaScript and SVG.

  4. Community and Ecosystem: D3.js has a larger and more active community compared to Bokeh. It has been around for a longer time and has gained popularity for its flexibility and versatility. D3.js has a vast ecosystem of plugins, examples, and tutorials available, making it easier for users to find resources and get help. Bokeh, although not as mature as D3.js, also has a growing community and provides comprehensive documentation and user-friendly interfaces.

  5. Integration with Backend Frameworks: Bokeh has better integration with popular backend frameworks like Flask and Django. It provides server-side functionalities, allowing users to build interactive visualizations that can receive and process data updates from the server. Bokeh also offers various deployment options, including standalone HTML files, embedded components, and server applications. D3.js, being a client-side library, requires additional backend infrastructure to handle data updates and server-side rendering.

  6. Browser Compatibility: Bokeh provides a higher level of compatibility with different web browsers. It abstracts away some of the browser-specific implementation details and provides a consistent experience across different environments. D3.js, being a lower-level library, may require additional effort to ensure compatibility and handle browser-specific quirks.

In summary, Bokeh is a Python-based library that provides a higher level of abstraction for data visualization with built-in interactivity and animation support, while D3.js is a JavaScript library that offers more flexibility and control over data manipulation, interactivity, and animation. Bokeh has better integration with backend frameworks and a more beginner-friendly approach, while D3.js has a larger community, extensive ecosystem, and potential for more customization and fine-grained control.

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Pros of Bokeh
Pros of D3.js
  • 12
    Beautiful Interactive charts in seconds
  • 195
    Beautiful visualizations
  • 103
  • 92
  • 81
    Large set of examples
  • 61
    Data-driven documents
  • 24
    Visualization components
  • 20
  • 18
    Dynamic properties
  • 16
  • 11
  • 7
    Makes data interactive
  • 4
    Open Source
  • 4
    Enter and Exit
  • 4
  • 3
  • 3
    Backed by the new york times
  • 2
    Easy and beautiful
  • 1
    Highly customizable
  • 1
    Awesome Community Support
  • 1
    Simple elegance
  • 1
    Templates, force template
  • 1
    Angular 4

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Cons of Bokeh
Cons of D3.js
    Be the first to leave a con
    • 11
      Beginners cant understand at all
    • 6
      Complex syntax

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    What is Bokeh?

    Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.

    What is D3.js?

    It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

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    What tools integrate with Bokeh?
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    Blog Posts

    Sep 8 2017 at 2:54PM


    What are some alternatives to Bokeh and D3.js?
    It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.
    It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.
    Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included.
    Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
    It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.
    See all alternatives