Book Image

Functional Python Programming. - Second Edition

Book Image

Functional Python Programming. - Second Edition

Overview of this book

If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.
Table of Contents (22 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Optimizing storage


There's no general rule for optimization. We often focus on optimizing performance because we have tools such as the Big O measure of complexity that show us whether or not an algorithm is an effective solution to a given problem. Optimizing storage is usually tackled separately: we can look at the steps in an algorithm and estimate the size of the storage required for the various storage structures.

In many cases, the two considerations are opposed. In some cases, an algorithm that has outstandingly good performance requires a large data structure. This algorithm can't scale without dramatic increases in the amount of storage required. Our goal is to design an algorithm that is reasonably fast and also uses an acceptable amount of storage.

We may have to spend time researching algorithmic alternatives to locate a way to make the space-time trade off properly. There are some common optimization techniques. We can often follow links from Wikipedia:

 http://en.wikipedia.org...