Data Structures and Algorithms in Python

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Michael T. Goodrich, Roberto Tamassia and Michael H. Goldwasser, “Data Structures and Algorithms in Python”
English | ISBN: 1118290275 | 2013 | PDF | 748 pages | 6 MB


This all-new Data Structures and Algorithms in Python was designed to provide an introduction to data structures and algorithms, including their design, analysis, and implementation. The authors use the beauty and simplicity of Python to give executable source code that's clear and concise. Furthermore, a regular object-oriented viewpoint is retained during the entire book, including the use of inheritance, both to maximise code reuse and to draw attention to the clear similarities and differences of varied abstract data types and algorithmic approaches.


This is often a “sister” book to Goodrich & Tamassia’s Data Structures and Algorithms in Java and Goodrich, Tamassia and Mount’s Data Structures and Algorithms in C++. This Python version retains much of the same pedagogical approach and general structure since the Java and C++ versions, to ensure that curriculums that teach data structures in Python, Java, and C++ can share the identical core syllabus.

Key Features of this Book
A primer that reviews the basics of programming in Python (Chapter 1), accompanied by a separate summary of object-oriented programming in Python (Chapter 2).
Extensive coverage of recursion (Chapter 4).
A chapter describing the array-based underpinnings of Python’s standard list, string, and tuple classes (Chapter 5), including both theoretical and empirical analyses with their efficiencies.
Source code with complete implementations with the majority of data structures and algorithms described inside the book; the code follows modern standards for Python 3, and makes using the standard collections module.
500 illustrations that present data structures and algorithms inside a clear, visual manner.
More than 750 exercises, divided into categories of reinforcement, creativity, and projects.


About the coverage:

The cover art will depend on an indigenous Australian painting style portraying what is known as Dreamtime. This style is traditionally iconic and representative of connections between sights or concepts; hence, it is a fitting method to capture with a high level the connections and sights used to visualize data structures and algorithms.


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