Summary

In this chapter, we explored different ways of performing exploratory data analysis, specifically focusing on population health information. With all the code provided in this book, the readers can definitely combine more datasets and explore the hidden characteristics. For instance, one can explore whether illegal drug usage is correlated with suicide, or whether exercise is anti-correlated with heart disease across the USA. One key message is that the readers should not mix up association and causality, which is a frequent mistake even made by experienced data scientists. Hopefully, by now, the readers are getting more comfortable with data analysis using Python, and we, the authors, are looking forward to your contribution to the Python community.

Happy coding!