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Python Parallel Programming Cookbook

You're reading from   Python Parallel Programming Cookbook Master efficient parallel programming to build powerful applications using Python

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Product type Paperback
Published in Aug 2015
Publisher
ISBN-13 9781785289583
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
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Table of Contents (8) Chapters Close

Preface 1. Getting Started with Parallel Computing and Python 2. Thread-based Parallelism FREE CHAPTER 3. Process-based Parallelism 4. Asynchronous Programming 5. Distributed Python 6. GPU Programming with Python Index

Handling map functions with SCOOP


A common task that is very useful when dealing with lists or other sequences of data is to apply the same operation to each element of the list and then collect the result. For example, a list update may be done in the following way from the Python IDLE:

>>>items = [1,2,3,4,5,6,7,8,9,10]
>>>updated_items = []
>>>for x in items:
>>>    updated_items.append(x*2)

>>> updated_items
>>>  [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

This is a common operation. However, Python has a built-in feature that does most of the work.

The Python function map(aFunction, aSequence) applies a passed-in function to each item in an iterable object and returns a list containing all the function call results. Now, the same example would be:

>>>items = [1,2,3,4,5,6,7,8,9,10]
>>>def multiplyFor2(x):return x*2
>>>print(list(map(multiplyFor2,items)))
>>>[2, 4, 6, 8, 10, 12, 14, 16, 18, 20]

Here, we...

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