Bokeh 简明教程

Bokeh - Extending Bokeh

Bokeh 可与多种其他库很好地集成，允许您针对每项任务使用最合适的工具。Bokeh 生成 JavaScript 这件事使其能将 Bokeh 输出与各种 JavaScript 库（如 PhosphorJS）结合使用。

Bokeh integrates well with a wide variety of other libraries, allowing you to use the most appropriate tool for each task. The fact that Bokeh generates JavaScript, makes it possible to combine Bokeh output with a wide variety of JavaScript libraries, such as PhosphorJS.

Datashader (https://github.com/bokeh/datashader) 是另一种可通过它扩展 Bokeh 输出的库。这是一个将大型数据集预渲染成大尺寸栅格图像的 Python 库。该功能克服了浏览器在处理非常大的数据时的局限性。Datashader 包括构建交互式 Bokeh 绘图的工具，这些绘图可在 Bokeh 中缩放和平移时动态重新渲染这些图像，使其在 Web 浏览器中处理任意大的数据集变得切实可行。

Datashader (https://github.com/bokeh/datashader) is another library with which Bokeh output can be extended. It is a Python library that pre-renders large datasets as a large-sized raster image. This ability overcomes limitation of browser when it comes to very large data. Datashader includes tools to build interactive Bokeh plots that dynamically re-render these images when zooming and panning in Bokeh, making it practical to work with arbitrarily large datasets in a web browser.

另一个库是 Holoviews ( (http://holoviews.org/) ，它提供了用于构建 Bokeh 绘图的简洁声明式界面，尤其是在 Jupyter Notebook 中。它便于快速制作数据分析图形的原型。

Another library is Holoviews ((http://holoviews.org/) that provides a concise declarative interface for building Bokeh plots, especially in Jupyter notebook. It facilitates quick prototyping of figures for data analysis.