Interactive Data Visualization in Python With Bokeh

Interactive Data Visualization in Python With Bokeh : Bokeh prides itself on being a library for interactive data visualization. The graphics are rendered using HTML and JavaScript, and your visualizations are easy to share as an HTML page. You will create a number of visualizations based on a real-world dataset.

If you love Python and want to impress your clients or your employer with impressive data visualization on the browser, Bokeh is the way to go. This course is a complete guide to mastering Bokeh, a Python library for building advanced and data dashboards containing beautiful interactive visualizations.

Bokeh is an interactive data visualization library for Python—and other languages—that targets modern web browsers for presentation. It can create versatile, data-driven graphics and connect the full power of the entire Python data science stack to create rich, interactive visualizations.

Interactive Data Visualization in Python With Bokeh

The course will guide you step by step from plotting simple datasets to building rich and beautiful data visualization web apps that plot data in real-time and allow web users to interact and change the behavior of your plots via the internet from their browsers.

This chapter provides an introduction to basic plotting with Bokeh. You will create your first plots, learn about different data formats Bokeh understands, and make visual customizations for selections and mouse hovering.

Interactive Data Visualization in Python With Bokeh

Bokeh server applications allow you to connect all of the powerful Python libraries for data science and analytics, such as NumPy and pandas to create rich, interactive Bokeh visualizations. Learn about Bokeh’s built-in widgets, how to add them to Bokeh documents alongside plots, and how to connect everything to real Python code using the Bokeh server.

Whether you are a data analyst, data scientist, statistician, or any other specialist who deals with data regularly, this course is perfect for you. It will give you the skills to visualize data in a way that excites your audience and eventually sells your product or your idea much easier. All you need to have to learn Bokeh is some basic prior knowledge of Python.

The goal of this course is to get you up and running with Bokeh.

What you’ll learn

You will learn how to:

• Transform your data into visualizations
• Customize and organize your visualizations
• Add interactivity to your visualizations

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Learn how to combine multiple Bokeh plots into different kinds of layouts on a page, how to easily link different plots together, and how to add annotations such as legends and hover tooltips.

In this final chapter, you’ll build a more sophisticated Bokeh data exploration application from the ground up based on the famous Gapminder dataset.

Interactive Data Visualization in Python With Bokeh

Unlike popular counterparts in the Python visualization space, like Matplotlib and Seaborn, Bokeh renders its graphics using HTML and JavaScript. This makes it a great candidate for building web-based dashboards and applications. However, it’s an equally powerful tool for exploring and understanding your data or creating beautiful custom charts for a project or report.

Using a number of examples on a real-world dataset, the goal of this tutorial is to get you up and running with Bokeh.

This course was created in collaboration with Anaconda. With over 6 million users, the open source Anaconda Distribution is the fastest and easiest way to do Python data science and machine learning. It’s the industry standard for developing, testing, and training on a single machine.

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