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.

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