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.
Bokeh 学习
原文：https://www.cnblogs.com/sisi-science/p/10798142.html
