Practical Data Science with Python

5

Exploratory Data Analysis and Visualization

We already briefly touched on exploratory data analysis (EDA) and visualization in the previous chapter, and now we will go deeper. EDA is a crucial step in any data science project because we need to understand our data to properly use it. EDA is iterative and happens continually throughout a project. As we learn more about how our data looks from analysis to modeling, we also need to incorporate more EDA to deepen our understanding.

Visualization goes hand in hand with EDA, and other books often show solely visual EDA. In this chapter, our EDA will focus on visualizations as well, since we already touched on numerical EDA in the previous chapter with pandas. However, visualization also involves ...