Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python
Study and use Python interactive libraries, such as Bokeh and Plotly
Explore different visualization principles and understand when to use which one
Create interactive data visualizations with real-world data
With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.
You’ll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You’ll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you’ll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You’ll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.
By the end of the course, you’ll have a new skill set that’ll make you the go-to person for transforming data visualizations into engaging and interesting stories.
What you will learn
Explore and apply different static and interactive data visualization techniques
Make effective use of plot types and features from the Matplotlib, Seaborn, Altair, Bokeh, and Plotly libraries
Master the art of selecting appropriate plotting parameters and styles to create attractive plots
Choose meaningful and informative ways to present your stories through data
Customize data visualization for specific scenarios, contexts, and audiences
Avoid common errors and slip-ups in visualizing data
Who this book is for
This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user’s attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.