Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques.
The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.
What youll learn
- How to transform new problems to well-known algorithmic problems with efficient solutions, or show that the problems belong to classes of problems thought not to be efficiently solvable
- How to analyze algorithms and Python programs using both mathematical tools and basic experiments and benchmarks
- How to understand several classical algorithms and data structures in depth, and be able to implement these efficiently in Python
- How to design and implement new algorithms for new problems, using time-tested design principles and techniques
- How to speed up implementations, using a plethora of tools for high-performance computing in Python
Who this book is for
The book is intended for Python programmers who need to learn about algorithmic problem-solving, or who need a refresher. Data and computational scientists employed to do big data analytic analysis should find this book useful. Game programmers and financial analysts/engineers may find this book applicable too. And, students of computer science, or similar programming-related topics, such as bioinformatics, may also find the book to be quite useful.
Table of Contents
Chapter 1. Introduction
Chapter 2. The Basics
Chapter 3. Counting 101
Chapter 4. Induction and Recursion … and Reduction
Chapter 5. Traversal: The Skeleton Key of Algorithmics
Chapter 6. Divide, Combine, and Conquer
Chapter 7. Greed Is Good? Prove It!
Chapter 8. Tangled Dependencies and Memoization
Chapter 9. From A to B with Edsger and Friends
Chapter 10. Matchings, Cuts, and Flows
Chapter 11. Hard Problems and (Limited) Sloppiness
Chapter 12. Pedal to the Metal: Accelerating Python
Chapter 13. List of Problems and Algorithms
Chapter 14. Graph Terminology!
Chapter 15. Hints for Exercises
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