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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

14.4 Using a multiprocessing pool for concurrent processing

One elegant way to make use of the multiprocessing module is to create a processing Pool object and assign work to the various workers in that pool. We will depend on the OS to interleave execution among the various processes. If each of the processes has a mixture of I/O and computation, we should be able to ensure that our processor (and disk) are kept very busy. When processes are waiting for the I/O to complete, other processes can do their computations. When an I/O operation finishes, the process waiting for this will be ready to run and can compete with others for processing time.

The recipe for mapping work to a separate process looks like this:

def demo_mp(root: Path = SAMPLE_DATA, pool_size: int | None = None) -> None: 
    pool_size = ( 
        multiprocessing.cpu_count() if pool_size is None 
       &...

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