Book Image

The Data Visualization Workshop

By : Mario Döbler, Tim Großmann
Book Image

The Data Visualization Workshop

By: Mario Döbler, Tim Großmann

Overview of this book

Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data? The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities. Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you’ll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You’ll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you’ve ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you’ll use the Bokeh library to create dynamic visualizations that can be integrated into any web page. By the end of this workshop, you’ll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data.
Table of Contents (9 chapters)
7. Combining What We Have Learned


In the previous chapter, we took an in-depth look at Matplotlib, one of the most popular plotting libraries for Python. Various plot types were covered, and we looked into customizing plots to create aesthetic plots.

Unlike Matplotlib, Seaborn is not a standalone Python library. It is built on top of Matplotlib and provides a higher-level abstraction to make visually appealing statistical visualizations. A neat feature of Seaborn is the ability to integrate with DataFrames from the pandas library.

With Seaborn, we attempt to make visualization a central part of data exploration and understanding. Internally, Seaborn operates on DataFrames and arrays that contain the complete dataset. This enables it to perform semantic mappings and statistical aggregations that are essential for displaying informative visualizations. Seaborn can also be used to simply change the style and appearance of Matplotlib visualizations.

The most prominent features of Seaborn are as follows...