Data Structures & Algorithms in Python PDF

Thể loại: Python;Lập trình
Tác giả : Michael T. Goodrich
Tóm tắt nội dung:

Data Structures and Algorithms in Python provides an introduction to data structures and algorithms, including their design, analysis, and implementation.

Giới thiệu sách

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Thông tin sách:

The design and analysis of efficient data structures has long been recognized as a vital subject in computing and is part of the core curriculum of computer science and computer engineering undergraduate degrees. Data Structures and Algorithms in Python provides an introduction to data structures and algorithms, including their design, analysis, and implementation. This book is designed for use in a beginning- level data structures course, or in an intermediate-level introduction to algorithms course. We discuss its use for such courses in more detail later in this preface.
To promote the development of robust and reusable software, we have tried to take a consistent object-oriented viewpoint throughout this text. One of the main ideas of the object-oriented approach is that data should be presented as being en- capsulated with the methods that access and modify them. That is, rather than simply viewing data as a collection of bytes and addresses, we think of data ob- jects as instances of an abstract data type (ADT), which includes a repertoire of methods for performing operations on data objects of this type. We then empha- size that there may be several different implementation strategies for a particular ADT, and explore the relative pros and cons of these choices. We provide complete Python implementations for almost all data structures and algorithms discussed, and we introduce important object-oriented design patterns as means to organize those implementations into reusable components.
Desired outcomes for readers of our book include that:
• They have knowledge of the most common abstractions for data collections
(e.g., stacks, queues, lists, trees, maps).
• They understand algorithmic strategies for producing efficient realizations of
common data structures.
• They can analyze algorithmic performance, both theoretically and experi-
mentally, and recognize common trade-offs between competing strategies.
• They can wisely use existing data structures and algorithms found in modern
programming language libraries.
• They have experience working with concrete implementations for most foun-
dational data structures and algorithms.
• They can apply data structures and algorithms to solve complex problems.
In support of the last goal, we present many example applications of data structures throughout the book, including the processing of file systems, matching of tags in structured formats such as HTML, simple cryptography, text frequency analy- sis, automated geometric layout, Huffman coding, DNA sequence alignment, and search engine indexing.

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