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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
Author Profile Icon Dusty Phillips
Dusty Phillips
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Toc

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

Runtime analysis


It should be becoming clear that an important aspect to algorithm design is gauging the efficiency both in terms of space (memory) and time (number of operations). This second measure, called runtime performance, is the subject of this section. It should be mentioned that an identical metric is used to measure an algorithm's memory performance. There are a number of ways we could, conceivably, measure run time and probably the most obvious is simply to measure the time the algorithm takes to complete. The major problem with this approach is that the time it takes for an algorithm to run is very much dependent on the hardware it is run on. A platform-independent way to gauge an algorithm's runtime is to count the number of operations involved. However, this is also problematic in that there is no definitive way to quantify an operation. This is dependent on the programming language, the coding style, and how we decide to count operations. We can use this idea, though, of...

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