In this chapter, we focused on pandas—a Python data analysis library. This was an introductory tutorial about the basic pandas features and data structures. We realized that a lot of the pandas functionality mimics relational database tables, allowing us to query, aggregate, and manipulate data efficiently. NumPy and pandas work well together and make it possible to perform basic statistical analysis. At this point, you might be tempted to think that pandas is all we need for data analysis. However, there is more to data analysis than meets the eye.

The next chapter, Chapter 5, Retrieving, Processing, and Storing Data, will teach us skills that are essential, though they may not be considered data analysis by some people. We will go with a broader definition that considers anything conceivably related to data analysis. Usually, when we analyze data, we don't have a whole team of assistants to help us with retrieving and storing data. However, since these tasks are important for a smooth data analysis flow, we will describe these activities in detail.