Summary

Congratulations, we have now learned how to use key features of the numpy library along with some practical real-world examples. We started by learning about arrays and why they are important by providing examples of how they have been rooted in computer science and programming languages for decades. We also learned about the foundation of structured data, which uses the concepts of arrays, by explaining the differences between single and multiple dimensional arrays and how we commonly identify them as tables with columns and rows.

Once the history and theories were explained, we learned how to make a NumPy array and walked through some useful functions available. We ended this chapter with a practical real-world example by loading stock prices into an array to show how it can answer specific questions by using a few NumPy commands available for data analysis. Data literacy skills were re-enforced throughout this chapter by understanding why data types impact data analysis and why the concept of KYD from Chapter 1, Fundamentals of Data Analysis, is important.

In the next chapter, we will expand your data literacy skills with some hands-on lessons working with data structures called DataFrames using the pandas library.