Data Visualization with Python

By Mario Döbler , Tim Großmann
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About this book

Data Visualization with Python reviews the spectrum of data visualization and its importance. Designed for beginners, it’ll help you learn about statistics by computing mean, median, and variance for certain numbers.

In the first few chapters, you’ll be able to take a quick tour of key NumPy and Pandas techniques, which include indexing, slicing, iterating, filtering, and grouping. The book keeps pace with your learning needs, introducing you to various visualization libraries. As you work through chapters on Matplotlib and Seaborn, you’ll discover how to create visualizations in an easier way. After a lesson on these concepts, you can then brush up on advanced visualization techniques like geoplots and interactive plots.

You'll learn how to make sense of geospatial data, create interactive visualizations that can be integrated into any webpage, and take any dataset to build beautiful visualizations. What’s more? You'll study how to plot geospatial data on a map using Choropleth plot and understand the basics of Bokeh, extending plots by adding widgets and animating the display of information.

By the end of this book, you’ll be able to put your learning into practice with an engaging activity, where you can work with a new dataset to create an insightful capstone visualization.

Publication date:
February 2019
Publisher
Packt
Pages
368
ISBN
9781789956467

About the Authors

  • Mario Döbler

    Mario Döbler is a graduate student with a focus in deep learning and AI. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of deep learning, using state-of-the-art algorithms to develop cutting-edge products. Currently, he dedicates himself to apply deep learning to medical data to make health care accessible to everyone.

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  • Tim Großmann

    Tim Großmann is a CS student with an interest in diverse topics ranging from AI to IoT. He previously worked at the Bosch Center for Artificial Intelligence in Silicon Valley in the field of big data engineering. He's highly involved in different open source projects and actively speaks at meetups and conferences about his projects and experiences.

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