Data Visualization in Python

David Landup
Dan Nelson


Data Visualization in Python, a course for beginner to intermediate Python developers, will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair.

Before diving too deep into the libraries themselves, we'll help you gain a better understanding of how the landscape of Python’s visualization libraries breaks down. To put that another way, it’s helpful to understand how the different Python libraries are designed and related to one another. Understanding how the different libraries operate will help you choose the best library for your visualization project.

We'll be covering:

  • Matplotlib-based libraries

  • JavaScript libraries

  • JSON libraries

  • WebGL libraries

More specifically, over the span of 11 chapters this course will cover 9 Python libraries: Pandas, Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGPlot, GeoPandas, and VisPy. Each library has its own unique features and quirks, some related to each other, while some are based on completely different technologies and ideas. That being said, this course will act as a one-stop in-depth resource for learning the ins and outs of each.

Whether you're a student or a seasoned developer, this course aims to get you on board with the current landscape of Data Visualization libraries in Python and up to speed with some of the most popular and powerful tools out there.


  • Introduction to Data Visualization

  • Types of Plots

  • Manipulating and Visualizing Data with Pandas

  • Matplotlib

  • Seaborn

  • Bokeh

  • Altair

  • Plotly

  • Ggplot

  • GeoPandas

  • VisPy


Downloadable Resources

Course Ebook (PDF)(15 MB)
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Course Ebook (EPUB)(21 MB)
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Last Updated: Mar 2022

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