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 (18 chapters)

Memoizing previous results with lru_cache


The lru_cache decorator transforms a given function into a function that might perform more quickly. The LRU means Least Recently Used—a finite pool of recently used items is retained. Items not frequently used are discarded to keep the pool to a bounded size.

Since this is a decorator, we can apply it to any function that might benefit from caching previous results. We can use it as follows:

from functools import lru_cache
@lru_cache(128)
def fibc(n: int) -> int:
    if n == 0: return 0
    if n == 1: return 1
    return fibc(n-1) + fibc(n-2)

This is an example based on Chapter 6, Recursions and Reductions. We've applied the @lru_cache decorator to the naive Fibonacci number calculation. Because of this decoration, each call to the fibc(n) function will now be checked against a cache maintained by the decorator. If the argument n is in the cache, the previously computed result is used instead of doing a potentially expensive re-calculation. Each...