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Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

By : Steven F. Lott
4.5 (28)
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Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

4.5 (28)
By: Steven F. Lott

Overview of this book

Not enough developers understand the benefits of functional programming, or even what it is. Author Steven Lott demystifies the approach, teaching you how to improve the way you code in Python and make gains in memory use and performance. If you’re a leetcoder preparing for coding interviews, this book is for you. Starting from the fundamentals, this book shows you how to apply functional thinking and techniques in a range of scenarios, with Python 3.10+ examples focused on mathematical and statistical algorithms, data cleaning, and exploratory data analysis. You'll learn how to use generator expressions, list comprehensions, and decorators to your advantage. You don't have to abandon object-oriented design completely, though – you'll also see how Python's native object orientation is used in conjunction with functional programming techniques. By the end of this book, you'll be well-versed in the essential functional programming features of Python and understand why and when functional thinking helps. You'll also have all the tools you need to pursue any additional functional topics that are not part of the Python language.
Table of Contents (18 chapters)
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Preface
16
Other Books You Might Enjoy
17
Index

5.3 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. When the predicate function’s result is true, the value is passed; otherwise, the value is rejected. The itertools module includes filterfalse() as a variation 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_legs = 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 contains 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...

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