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

Using the filter() function to pass or reject data

The job of the filter() function is to use and apply a decision function called a predicate to each value in a collection. A decision of True means that the value is passed; otherwise, the value is rejected. The itertools module includes filterfalse() as variations on this theme. Refer to Chapter 8, The Itertools Module, to understand the usage of the itertools module's filterfalse() function.

We might apply this to our trip data to create a subset of legs that are over 50 nautical miles long, as follows:

long= list(
     filter(lambda leg: dist(leg) >= 50, trip))

The predicate lambda will be True for long legs, which will be passed. Short legs will be rejected. The output is the 14 legs that pass this distance test.

This kind of processing clearly segregates the filter rule (lambda leg: dist(leg) >= 50) from any other processing that creates the trip object or analyzes the long legs.

For another simple example, look at the following...