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Bokeh vs Matplotlib: What are the differences?

Bokeh vs Matplotlib: Key Differences

Bokeh and Matplotlib are two popular visualization libraries used in Python for creating interactive and static plots respectively. While both libraries serve the purpose of data visualization, there are several key differences between Bokeh and Matplotlib.

  1. Ease of Use: Bokeh simplifies the process of creating interactive visualizations by allowing users to create plots with only a few lines of code, while Matplotlib requires more code to achieve the same level of interactivity. Bokeh provides a higher level of abstraction, making it easier for users to build interactive plots quickly and efficiently.

  2. Rendering: Bokeh renders plots using JavaScript, HTML, and CSS, whereas Matplotlib generates static images in various formats such as PNG, PDF, and SVG. This fundamental difference in rendering mechanisms gives Bokeh an advantage when it comes to creating interactive visualizations that can be easily embedded in web applications.

  3. Interactivity: Bokeh focuses on providing interactivity out of the box, allowing users to easily add tools like zooming, panning, and hover tooltips to their plots. Matplotlib, on the other hand, requires users to manually add interactivity to their plots by writing custom code.

  4. Backends: Matplotlib supports a variety of backends, including Tk, GTK, and Qt, which allow users to choose the most suitable backend for their specific needs. Bokeh, on the other hand, mainly relies on a web-based interface and is designed to work well with modern web browsers.

  5. Integration with Web Frameworks: Bokeh seamlessly integrates with popular web frameworks like Flask and Django, allowing users to embed interactive plots directly into their web applications. Matplotlib, on the other hand, is primarily used for generating static plots and does not offer the same level of integration with web frameworks.

  6. Performance: When it comes to performance, Matplotlib has an advantage over Bokeh, especially for generating static plots. Matplotlib is a highly optimized library with a wide range of backend options, enabling it to generate plots quickly. Bokeh, on the other hand, relies on web technologies for rendering, which can introduce some overhead and impact performance, particularly for large datasets.

In summary, Bokeh and Matplotlib differ in terms of ease of use, rendering mechanisms, interactivity support, backends, integration with web frameworks, and performance characteristics. Understanding these key differences can help users choose the most suitable library for their specific data visualization requirements.

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Pros of Bokeh
Pros of Matplotlib
  • 12
    Beautiful Interactive charts in seconds
  • 10
    The standard Swiss Army Knife of plotting

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Cons of Bokeh
Cons of Matplotlib
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    • 5
      Lots of code

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    - No public GitHub repository available -

    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 Matplotlib?

    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.

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    What tools integrate with Bokeh?
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    What are some alternatives to Bokeh and Matplotlib?
    Plotly.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 Plotly.py 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.
    Dash
    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.
    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.
    Tableau
    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.
    Shiny
    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