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


NumPy and pandas are essential tools for data wrangling. Their user-friendly interfaces and performant implementation make data handling easy. Even though they only provide a little insight into our datasets, they are valuable for wrangling, augmenting, and cleaning our datasets. Mastering these skills will improve the quality of your visualizations.

In this chapter, we learned about the basics of NumPy, pandas, and statistics. Even though the statistical concepts we covered are basic, they are necessary to enrich our visualizations with information that, in most cases, is not directly provided in our datasets. This hands-on experience will help you implement the exercises and activities in the following chapters.

In the next chapter, we will focus on the different types of visualizations and how to decide which visualization would be best for our use case. This will give you theoretical knowledge so that you know when to use a specific chart type and why. It will also lay down the fundamentals of the remaining chapters in this book, which will focus on teaching you how to use Matplotlib and seaborn to create the plots we have discussed here. After we have covered basic visualization techniques with Matplotlib and seaborn, we will dive more in-depth and explore the possibilities of interactive and animated charts, which will introduce an element of storytelling into our visualizations.