Book Image

Functional Python Programming - Second Edition

By : Steven F. Lott
Book Image

Functional Python Programming - Second Edition

By: Steven F. Lott

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

Writing higher-order mappings and filters

Python's two built-in higher-order functions, map() and filter(), generally handle almost everything we might want to throw at them. It's difficult to optimize them in a general way to achieve higher performance. We'll look at functions of Python 3.4, such as imap(), ifilter(), and ifilterfalse(), in Chapter 8, The Itertools Module.

We have three, largely equivalent ways to express a mapping. Assume that we have some function, f(x), and some collection of objects, C. We have three entirely equivalent ways to express a mapping; they are as follows:

  • The map() function:
map(f, C) 
  • A generator expression:
(f(x) for x in C) 
  • A generator function with a yield statement:
def mymap(f, C): 
    for x in C: 
        yield f(x) 
mymap(f, C) 

Similarly, we have three ways to apply a filter function to a collection, all of which are equivalent:

  • The filter() function:
filter(f, C) 
  • A generator expression:
(x for x in C if f(x)) 
  • A generator function with a yield statement...