Table of contents
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.
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.
We have collected here some useful links to help you find out if Bokeh is good.
Check the traffic stats of Bokeh on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Bokeh on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Bokeh's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Bokeh on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Bokeh on Reddit. This can help you find out how popualr the product is and what people think about it.
Visualization: https://docs.bokeh.org/en/latest/. Source: over 3 years ago
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 3 years ago
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 on the Grammar of Graphics. It has the added advantage of managing real-time data and streaming. This library can be used for creating common charts such as histograms, bar plots,... - Source: dev.to / over 3 years ago
It's not in the least bit "underrated" and it's documentation is extensive. Source: over 4 years ago
Hi guys! I am currently working on a project to enrich my Master thesis with some interactive plots. I have been using the Bokeh library to make a standalone application, which I was then planning to deploy in Heroku. You can find the code in this repository. But I will also add it at the bottom of the post. Source: over 4 years ago
Bokeh, an increasingly popular open-source library, is recognized for its abundant capabilities in enabling interactive and browser-friendly data visualization. Esteemed as a part of Python's robust visualization ecosystem, Bokeh distinguishes itself by allowing users to create dynamic visual representations of data that can seamlessly integrate into web applications. Public opinion echoes a positive sentiment, largely commending Bokeh for its versatility and comprehensiveness in handling complex visualization tasks.
Bokeh stands out with its three-tiered user interface design, catering to users ranging from novices to seasoned developers. This tiered approach ensures that users at all skill levels can efficiently utilize Bokeh: from crafting simple visualizations to engaging with more intricate tasks that demand precise customization. Enthusiasts have frequently highlighted this gradation, appreciating how it aligns with varying complexity and expertise, providing an accessible entry point for beginners while offering depth for advanced users.
A notable aspect of Bokeh is its implementation of real-time data visualization and streaming capabilities. This feature positions the library as exceptionally valuable for applications that require up-to-the-minute data insights—a necessity in today's fast-paced data-driven environments. Users value Bokeh's ability to generate interactive plots that are not only aesthetically pleasing but highly functional in application contexts, particularly for projects aiming for deployment in cloud platforms like Heroku.
Despite Bokeh's comprehensive documentation, as evidenced by references within its community and user-generated reviews, a common claim debated among practitioners is whether its documentation can be perceived as underrated. On one hand, users appreciate the dedication to detail and structure found in the existing resources, while on the other, some feel that its extensive nature might occasionally overwhelm new users unfamiliar with its vast potential.
When deliberating over Bokeh's position against competitors such as Plotly, D3.js, and Highcharts, its unique proposition lies in its native Python support and comparative readiness for web integration. Unlike Pygal's SVG focus or D3.js' extensive coding requirements, Bokeh offers diverse output options, including HTML and JSON, ensuring accessibility and adaptability to various environments without compromising quality or interactivity.
The public discourse surrounding Bokeh often draws parallels to its competitor libraries, underscoring its suitability for those prioritizing Python as their primary development language. With continuous iterations and updates from its contributors, Bokeh remains a pivotal tool in the data visualization segment, increasingly favored for projects that necessitate intricate, interactive plotting capabilities.
In summation, Bokeh is lauded in the public domain for its rich feature set that supports a spectrum of visualization needs. Its ability to bridge simplicity and sophistication makes it a compelling choice for data enthusiasts and professionals keen on cultivating powerful, interactive visual narratives that resonate well with the audiences they aim to engage.
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Is Bokeh good? This is an informative page that will help you find out. Moreover, you can review and discuss Bokeh here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
