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Published: May 05, 2015
Last Updated: Oct 28, 2020
Oct 2020

Bokeh 是 Python 中最重要的库之一,通过 JavaScript 在浏览器中的渲染,它可以用于科学绘制和数据可视化。与创建出静态图像的桌面工具相比,这样的工具更易于在 web 应用探索中重用代码。Bokeh 尤其擅长这一点。这个库已经足够成熟,功能齐全。我们喜爱 Bokeh 之处在于:它保持对于作为展示层工具的专注,不会越界到比如数据聚合(参照 ggplot)或者 web 应用开发(参照 Shiny或者 Dash)。所以当分离关注点对你来说很重要时,使用 Bokeh 就是件很愉悦的事情了。Boken 提供了web UI 小部件,并能运行于服务器模式,但你可以伺机使用或者放弃这些特性。Bokeh 很灵活,使用方式很直白,它也没有那么多依赖(比如 pandas或者 notebooks)。

May 2015

In the world of data science and analytics, much of the work is done using Python and R, languages which sadly offer few options for web-accessible plotting of visualizations. One approach is to convert the result of analysis into something that can be easily visualized and interacted with in the browser. We’re aware of two tools that are an attempt to do this. Bokeh is a Python and JavaScript library that allows you to create interactive visualizations “in the style of D3.js” but with high performance over large or streaming data sets. Vega is a declarative visualization grammar for D3 that consumes server-generated JSON datasets and translates visualization descriptions into D3.js code.