Chapter 3: Visualization with Statistical Graphs
A picture is worth a thousand words. Humans rely on visual input for more than 90% of all information obtained. A statistical graph can demonstrate trends, explain reasons, or predict futures much better than words if done right.
Python data ecosystems come with a lot of great tools for visualization. The three most important ones are Matplotlib, seaborn, and plotly. The first two are mainly for static plotting, while plotly is capable of interactive plotting and is gaining in popularity gradually.
In this chapter, you will focus on static plotting, which is the backbone of data visualization. We have already extensively used some plots in previous chapters to illustrate concepts.
In this chapter, we will approach them in a systematic way. The topics that will be covered in this chapter are as follows:
- Picking the right plotting types for different tasks
- Improving and customizing visualization with advanced aesthetic...
