We have reached the end of our journey in learning about pandas and the features it offers for data manipulation. Prior to this chapter, we spent our time mostly learning the features, most of the time using data designed to demonstrate the concepts instead of using real-world data.

In this chapter, we used everything that we learned up to this point to demonstrate how easy it is to use pandas to analyze real-world data—specifically, stock data—and derive results from the data, and in many cases, make quick conclusions through visualizations designed to make the patterns in the data apparent.

This chapter also introduced a number of financial concepts, such as the daily percentage change, calculating returns, and the correlation of time-series data, among several others. The focus was not on financial theory but to demonstrate how easy it is to use pandas to manage and derive meaning from what was otherwise just lists and lists of numbers.

In closing, it is worth noting that although pandas was created by financial analysts—hence its ability to provide simple solutions in the financial domain—pandas is in no way limited to just finance. It is a very robust tool for data science and can be applied just as effectively to many other domains. Several of these are emerging markets with significant opportunity, such as social network analysis or applications of wearable computing, such as fitness data collected continuously and used to make people healthier. Whatever your domain of use for pandas, I hope you find using pandas as fascinating as I do.