Bokeh 学习

这段时间由于在做K-means对文本进行处理，需要进行绘图，实验室编程大哥向我介绍了Bokeh来进行绘图，一直是根据自己的需求来进行对其探索，今儿个看到一篇博文，对Bokeh进行了详细的解说，做个笔记。

博文非常适合Bokeh的初级学者，原文链接如下：https://cloud.tencent.com/developer/article/1134383

Bokeh是一个专门针对Web浏览器的呈现功能的交互式可视化Python库。这是Bokeh与其它可视化库最核心的区别。正如下图所示，它说明了Bokeh如何将数据展示到一个Web浏览器上的流程。

Bokeh的优势：

• Bokeh允许你通过简单的指令就可以快速创建复杂的统计图，
• Bokeh提供到各种媒体，如HTML，Notebook文档和服务器的输出
• ·我们也可以将Bokeh可视化嵌入flask和django程序
• Bokeh可以转换写在其它库（如matplotlib, seaborn和ggplot）中的可视化
• ·Bokeh能灵活地将交互式应用、布局和不同样式选择用于可视化

用Bokeh实现可视化

Bokeh提供了强大而灵活的功能，使其操作简单并高度定制化。它为用户提供了多个可视化界面，如下图所示

• 图表（Charts）：一个高级接口（high-level interface），用以简单快速地建立复杂的统计图表。
• 绘图（Plotting）：一个中级接口（intermediate-level interface），以构建各种视觉符号为核心。
• 模块（Models）：一个低级接口（low-level interface），为应用程序开发人员提供最大的灵活性。

The full list of sections for all the modules in Bokeh is accessible from the sidebar to the left. Listed below are a few selected sections that may be especially useful.

bokeh.models

Everything that comprises a Bokeh plot or application—tools, controls, glyphs, data sources—is a Bokeh Model. Bokeh models are configured by setting values their various properties. This large section has a reference for every Bokeh model, including information about every property of each model.

bokeh.plotting

The bokeh.plotting API is centered around the figure() command, and the associated glyph functions such as circle(), wedge(), etc. This section has detailed information on these elements.

bokeh.layouts

The simplest way to combine multiple Bokeh plots and controls in a single document is to use the layout functions such as row(), column(), etc. from the bokeh.layouts module.

bokeh.io

Functions for controlling where and how Bokeh documents are saved or shown, such as output_file(), output_notebook(), and others are in this module.

bokeh.palettes

This section provides visual representations of all the palettes built into Bokeh.

bokeh.settings

All Bokeh-related environment variables, which can be used to control things like resources, minification, and log levels, are documented here.