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

Geospatial Visualizations

Voronoi tessellation, Delaunay triangulation, and choropleth plots are a few of the geospatial visualizations that will be used in this chapter. An explanation for each of them is provided here.

Voronoi Tessellation

In a Voronoi tessellation, each pair of data points is separated by a line that is the same distance from both data points. The separation creates cells that, for every given point, marks which data point is closer. The closer the data points, the smaller the cells.

The following example shows how you can simply use the voronoi method to create this visualization:

# plotting our dataset as voronoi plot
geoplotlib.voronoi(dataset_filtered, line_color='b')

As we can see, the code to create this visualization is relatively short.

After importing the dependencies we need, we read the dataset using the read_csv method of pandas (or geoplotlib). We then use it as data for our...