Summary

This chapter covered the most important visualizations, categorized into comparison, relation, composition, distribution, and geological plots. For each plot, a description, practical examples, and design practices were given. Comparison plots, such as line charts, bar charts, and radar charts, are well suited to comparing multiple variables or variables over time. Relation plots are perfectly suited to show relationships between variables. Scatter plots, bubble plots, which are an extension of scatter plots, correlograms, and heatmaps were considered.

Composition plots are ideal if you need to think about something as part of a whole. We first covered pie charts and continued with stacked bar charts, stacked area charts, and Venn diagrams. For distribution plots that give a deep insight into how your data is distributed, histograms, density plots, box plots, and violin plots were considered. Regarding geospatial data, we discussed dot maps, connection maps, and choropleth maps. Finally, some remarks were provided on what makes a good visualization.

In the next chapter, we will dive into Matplotlib and create our own visualizations. We will start by introducing the basics, followed by talking about how you can add text and annotations to make your visualizations more comprehensible. We will continue creating simple plots and using layouts to include multiple plots within a visualization. At the end of the next chapter, we will explain how you can use Matplotlib to visualize images.