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Bokeh vs Highcharts: What are the differences?

  1. Integration with programming languages: Bokeh can be seamlessly integrated with Python, making it a preferred choice for data scientists and Python developers. On the other hand, Highcharts is primarily written in JavaScript and works well with web development technologies like HTML, CSS, and JavaScript.

  2. Customization options: Bokeh provides a higher degree of customization options, including advanced plotting tools and vector graphic export capabilities. Highcharts, although feature-rich, may have limitations in terms of customization when compared to Bokeh.

  3. Community support and resources: Highcharts has a larger community base and extensive documentation, making it easier to find examples, tutorials, and support online. Bokeh, being a Python library, also has a supportive community but may not be as extensive as Highcharts.

  4. License and pricing: Bokeh is an open-source library that is distributed under the BSD-3-Clause license, making it free for commercial and non-commercial use. Highcharts, on the other hand, has different licensing options, including a free for non-commercial use license and commercial licenses for enterprise use.

  5. Performance and rendering: Highcharts is known for its performance and rendering speed, especially when dealing with a large amount of data. Bokeh, while capable of handling complex visualizations, may not match the speed and efficiency of Highcharts in certain scenarios.

  6. Chart types and features: Highcharts offers a wide variety of chart types and features out of the box, making it a versatile choice for creating different types of visualizations. Bokeh, while also versatile, may require more manual interventions to achieve certain complex chart types or features that are readily available in Highcharts.

In Summary, Bokeh and Highcharts differ in their integration with programming languages, customization options, community support, licensing, performance, and chart types and features available.

Advice on Bokeh and Highcharts
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I have used highcharts and it is pretty awesome for my previous project. now as I am about to start my new project I want to use other charting libraries such as recharts, chart js, Nivo, d3 js.... my upcoming project might use react js as front end and laravel as a backend technology. the project would be of hotel management type. please suggest me the best charts to use

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Darren Adams
Senior Developer at Burning Glass Technologies · | 2 upvotes · 139.2K views
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I've used Highcharts with both Angular Js Reactive applications (render as ReactJs) and also a bit of D3. Personally I found Highcharts to be the easiest to use but, with still quite a good level of customisability if you need it. graphs and charts then give D3 a try.

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Pros of Bokeh
Pros of Highcharts
  • 12
    Beautiful Interactive charts in seconds
  • 34
    Low learning curve and powerful
  • 17
    Multiple chart types such as pie, bar, line and others
  • 13
    Responsive charts
  • 9
    Handles everything you throw at it
  • 8
    Extremely easy-to-parse documentation
  • 5
    Built-in export chart as-is to image file
  • 5
    Easy to customize color scheme and palettes
  • 1
    Export on server side, can be used in email

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Cons of Bokeh
Cons of Highcharts
    Be the first to leave a con
    • 9
      Expensive

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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 Highcharts?

    Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

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    What companies use Bokeh?
    What companies use Highcharts?
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    What tools integrate with Bokeh?
    What tools integrate with Highcharts?

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    What are some alternatives to Bokeh and Highcharts?
    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.
    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.
    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.
    See all alternatives