Introduction

In the previous chapter, we learned how to handle pandas DataFrames as inputs for data visualization, how to plot with pandas and seaborn, and how to refine plots to increase their aesthetic appeal. The intent of this chapter is to acquire practical knowledge about the strengths and limitations of various visualization techniques. We'll practice creating plots for a variety of different contexts. However, you will notice that the variety in existing plot types and visualization techniques is huge, and choosing the appropriate visualization becomes confusing. There are times when a plot shows too much information for the reader to grasp or too little for the reader to get the necessary intuition regarding the data. There are times when a visualization is too esoteric for the reader to appreciate properly, and other times when an over-simplistic visualization just doesn't have the right impact. All these scenarios can be avoided by being armed with practical knowledge about the interpretation of different kinds of visualization techniques and their strengths and limitations.

This chapter is a primer on the different types of static visualization and the contexts in which they are most effective. Using seaborn, you will learn how to create a variety of plots and become proficient in selecting the right kind of visualization for the most suitable representation of your data. Combining these skills with the techniques learned in Chapter 1, Introduction to Visualization with Python – Basic and Customized Plotting, will help you make stellar plots that are both meaningful and attractive.

Let's first explore the right kind of visualization technique or plot to represent global patterns in data.

Note

Some of the images in this chapter have colored notations, you can find high-quality color images used in this chapter at: https://github.com/TrainingByPackt/Interactive-Data-Visualization-with-Python/tree/master/Graphics/Lesson2.