Congratulations on making it through this chapter! Data wrangling isn't the most exciting part of the analytics workflow, but we will spend a lot of time on it, so it's best to be well-versed in what pandas has to offer. In this chapter, we learned more about what data wrangling is (aside from a data science buzzword) and got some firsthand experience with data cleaning and reshaping our data. Utilizing the requests library, we once again practiced working with APIs to extract data of interest; then, we used pandas to begin our data wrangling, which we will continue in the next chapter. Finally, we learned how to deal with duplicate, missing, and invalid data points in various ways and discussed the ramifications of those decisions.
In the next chapter, we will learn how to aggregate dataframes.
