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 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.

In the subsequent chapters, we will learn how to improve performance further using numerical libraries such as numpy, and how to write extension modules in a lower-level language with the help of Cython.