Congratulations on making it through this chapter! Data wrangling may not be 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 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 introduction to 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.
Building on these concepts, in the next chapter, we will learn how to aggregate dataframes and work with time series data. Be sure to complete the end-of-chapter exercises before moving on.