Summary

Algorithmic optimization can improve how your application scales as we process increasingly large data. In this chapter, we demonstrated use cases and running times of the most common data structures available in Python, such as lists, deques, dictionaries, heaps, and tries. We also covered caching, a technique that can be used to trade some space, in memory or on disk, in exchange for the increased responsiveness of an application. We also demonstrated how to get modest speed gains by replacing for loops with fast constructs, such as list comprehensions and generator expressions.

Overall, we have seen that by utilizing a specifically designed data structure or technique that is appropriate in certain situations, the efficiency of our program can be greatly improved. The topics covered in this chapter offer us the ability to do just that across a wide range of use cases.

In the subsequent chapters, we will learn how to improve performance further using numerical libraries...