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)
Preface
7
7. Combining What We Have Learned

Summary

In this chapter, we demonstrated how Seaborn helps to create visually appealing figures. We discussed various options for controlling Figure aesthetics, such as Figure style, controlling spines, and setting the context of visualizations. We talked about color palettes in detail. Further visualizations were introduced for univariate and bivariate distributions. Moreover, we discussed FacetGrids for creating multi-plots, and regression plots as a way to analyze the relationships between two variables. Finally, we discussed the Squarify library, which is used to create tree maps.

In the next chapter, we will work with a different category of data, called geospatial data. The prominent attribute of such a dataset is the presence of geo-coordinates that can be used to plot elements on a given position on a map. We will visualize poaching points, the density of cities around the world, and create a more interactive visualization that only displays data points of the currently...