Titlebook: Data Structures and Algorithms with Python; Kent D. Lee,Steve Hubbard Textbook 20151st edition Springer Nature Switzerland AG 2015 Algorit

insipid

Data Structures and Algorithms with Python978-3-319-13072-9Series ISSN 1863-7310 Series E-ISSN 2197-1781

Abutment

1863-7310 ts of computational complexity, from the basics to amortizedThis textbook explains the concepts and techniques required to write programs that can handle large amounts of data efficiently. Project-oriented and classroom-tested, the book presents a number of important algorithms supported by examples

consent

elastic

Balanced Binary Search Trees,ees by G.M. Adelson-Velskii and E.M. Landis, two Soviet computer scientists, in 1962. AVL trees were named for these two inventors. Their paper on AVL trees [AVL62] described the first algorithm for maintaining balanced binary search trees. The chapter goes on to discuss Splay Trees as another example of balanced binary search trees.

Barter

B-Trees,s. It goes on to introduce the B-Tree data structure and its implementation. By the end of this chapter you should have an understanding of B-Trees, their advantages over other data structures, and you should be able to demonstrate your understanding by implementing a B-Tree that can be used to efficiently process em joins in relational databases.

STING

Lecture Notes in Computer Science reading the rest of the text. The PyList datatype presented in this chapter is also used in subsequent chapters including Chap. 2. Even if the reader has worked through an introductory text or has a basic understanding of Python, there are concepts from this first chapter that are used throughout the text.

先兆

LEVER

Lecture Notes in Computer Sciencechapter we’ll motivate our interest in hashing, then we’ll develop a hashing algorithm for finding values in a set. We’ll also apply hashing to the building of sets and maps. Then we’ll look at an important technique that uses hashing called . and we’ll apply that technique to a couple of problems.

convulsion

Cumbersome

Sequences,nizing data into a sequence. We’ll also examine how to use Python to make working with sequences convenient. Operator overloading in Python lets us build sequences that we can manipulate with intuitive operations. Finally, we also examine how the organization of a sequence affects the computation complexity of operations on it.