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Getting Started with Python

You're reading from   Getting Started with Python Understand key data structures and use Python in object-oriented programming

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Product type Course
Published in Feb 2019
Publisher Packt
ISBN-13 9781838551919
Length 722 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Baka Benjamin Baka
Author Profile Icon Benjamin Baka
Benjamin Baka
Fabrizio Romano Fabrizio Romano
Author Profile Icon Fabrizio Romano
Fabrizio Romano
Dusty Phillips Dusty Phillips
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Dusty Phillips
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Table of Contents (31) Chapters Close

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. A Gentle Introduction to Python FREE CHAPTER 2. Built-in Data Types 3. Iterating and Making Decisions 4. Functions, the Building Blocks of Code 5. Files and Data Persistence 6. Principles of Algorithm Design 7. Lists and Pointer Structures 8. Stacks and Queues 9. Trees 10. Hashing and Symbol Tables 11. Graphs and Other Algorithms 12. Searching 13. Sorting 14. Selection Algorithms 15. Object-Oriented Design 16. Objects in Python 17. When Objects Are Alike 18. Expecting the Unexpected 19. When to Use Object-Oriented Programming 20. Python Object-Oriented Shortcuts 21. The Iterator Pattern 22. Python Design Patterns I 23. Python Design Patterns II 24. Testing Object-Oriented Programs 1. Other Books You May Enjoy Index

Anonymous functions


One last type of functions that I want to talk about are anonymous functions. These functions, which are called lambdas in Python, are usually used when a fully-fledged function with its own name would be overkill, and all we want is a quick, simple one-liner that does the job.

 

 

Imagine that you want a list of all the numbers up to N that are multiples of five. Imagine that you want to filter those out using the filter function, which takes a function and an iterable and constructs a filter object that you can iterate on, from those elements of iterables for which the function returns True. Without using an anonymous function, you would do something like this:

# filter.regular.py
def is_multiple_of_five(n):
    return not n % 5

def get_multiples_of_five(n):
    return list(filter(is_multiple_of_five, range(n)))

Note how we use is_multiple_of_five to filter the first n natural numbers. This seems a bit excessive, the task is simple and we don't need to keep the is_multiple_of_five...

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