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Bokeh – Introduction. This topic will help you in understanding Bokeh which is a data visualization library for Python. Here, you will learn about how to use Bokeh to create data applications, interactive plots, and dashboards.

This topic will give you enough understanding of the various functionalities of Bokeh with illustrative examples.

Bokeh – Introduction

Bokeh is a data visualization library for Python that can easily connect with these tools and produce interactive plots, dashboards, and data applications.

Features

Some of the important features of Bokeh are as follows −

Flexibility

Bokeh is useful for common plotting requirements as well as custom and complex use-cases.

Productivity

Bokeh can easily interact with other popular Pydata tools such as Pandas and Jupyter notebooks.

Interactivity

This is an important advantage of Bokeh over Matplotlib and Seaborn, both produce static plots. You can give your audience a wide range of options and tools for inferring and looking at data from various angles so that user can perform “what if” analysis.

Powerful

By adding custom JavaScript, it is possible to generate visualizations for specialized use-cases.

Sharable

Plots can be embedded in output of Flask or Django enabled web applications. They can also be rendered in

Jupyternotebooks.

Open source

Bokeh is an open-source project. Its source code is available on https://github.com/bokeh/bokeh.

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