Bokeh is a flexible and open-source visualization library. It provides several tools and widgets to maximize interactivity for the audience. It is used to create plots. It is also suitable for special use cases. It is designed to be easily sharable. It provides plots, dashboards and apps that can be published with ease. Bokeh is popular for providing a secure and conducive space for building sophisticated data applications. It has a reliable ecosystem and comes with several plot tools. Bokeh is widely used by scientists and developers for its user-friendliness. It helps users explore the data in Jupyter Lab and classic notebooks with ease. It also helps with the visualization of streaming data from financial markets, physical sensors and IoT telemetry.
1,265
websites using Bokeh
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Websites using Bokeh by countrymarket share · top 8 countries
🇩🇪Germany
🇬🇧United Kingdom
🇫🇷France
🇺🇸United States
🇳🇱Netherlands
🇮🇹Italy
🇮🇩Indonesia
🇪🇸Spain
0%2%4%6%8%
Websites using Bokeh by industrydetected industries
Photography30%
Technology & Computing26%
Movies & TV13%
Design & Creative12%
Marketing & Advertising9.9%
Education8.8%
Websites using Bokeh
showing 25 of 1,265| Website | Country | Industry | Size | Founded | |
|---|---|---|---|---|---|
| 🇺🇸US | Technology & Computing | 11-50 | 2013 | ||
| 🇺🇸US | Hospitality & Restaurants | 1-10 | - | ||
| 🇬🇧GB | Design & Creative | 1-10 | 2017 | ||
| 🇬🇧GB | Entertainment | 51-200 | 1995 | ||
| - | Shopping | 5001-10000 | - | ||
| 🇳🇿NZ | Software Development | 51-200 | 1998 | ||
| - | Photography | 1-10 | 2013 | ||
| 🇺🇸US | Technology & Computing | 1-10 | - | ||
| 🇦🇺AU | Design & Creative | 1-10 | 2024 | ||
| 🇫🇷FR | Technology & Computing | 51-200 | 2017 | ||
| 🇪🇸ES | Gaming | 11-50 | - | ||
| 🇩🇪DE | Marketing & Advertising | 1-10 | - | ||
| 🇫🇷FR | Design & Creative | 1-10 | 2012 | ||
| 🇨🇦CA | Education | 1-10 | 2009 | ||
| 🇬🇧GB | Design & Creative | 11-50 | 2004 | ||
| 🇨🇴CO | Education | 201-500 | 1978 | ||
| 🇮🇷IR | Society | 1-10 | 2016 | ||
| 🇪🇸ES | - | 1-10 | - | ||
| 🇪🇸ES | Technology & Computing | 1-10 | 2007 | ||
| 🇮🇳IN | Technology & Computing | 1-10 | 2015 | ||
| 🇬🇧GB | Print & Publishing | 1-10 | 2017 | ||
| 🇫🇷FR | Education | 11-50 | - | ||
| 🇫🇷FR | - | 1-10 | - | ||
| 🇺🇸US | Technology & Computing | 1-10 | 2013 | ||
| 🇧🇪BE | - | 1-10 | - |
Showing 25 of 1,265 websitesGet the full list →
Frequently asked
What is Bokeh in data visualization?›
Bokeh is an interactive data visualization library in Python that provides versatile, elegant, and high-performance graphics tools. It enables users to create web-based plots with simple or complex interactions and dashboards by generating HTML, CSS, and JavaScript output. Bokeh is suitable for constructing aesthetic visualizations to display large datasets efficiently.
Can Bokeh be integrated with other Python libraries?›
Yes, Bokeh can be integrated with multiple popular Python libraries, including NumPy, Pandas, Datashader, HoloViews, and others. This integration allows users to create more sophisticated visualizations and take full advantage of the Python data science ecosystem.
How does Bokeh compare to other visualization libraries like Matplotlib and Plotly?›
Bokeh differs from other visualization libraries, such as Matplotlib and Plotly, in its focus on providing robust and high-performance interactive plots in a web browser. While Matplotlib excels in creating static images, and Plotly specializes in online interactive visualizations, Bokeh balances these aspects by offering versatile visualization options with interactive features.
Is it possible to create a stand-alone HTML file with my Bokeh visualization?›
Yes, Bokeh allows users to create stand-alone HTML files containing their visualizations. By using the output_file() function along with the save() or show() function, the plots can be either saved to an HTML file or displayed in a new browser window as a self-contained file. This feature enables easy sharing and embedding of Bokeh plots.
Can I customize the appearance and style of my Bokeh plots?›
Absolutely! Bokeh provides abundant options for users to customize their visualizations, including modifying the plot title, axes labels, color palettes, background color, and gridlines. Additionally, the library supports the integration of custom CSS styling, allowing even more control over the appearance of the plots.
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