04342nam a22005655i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172050001300187072001600200072001600216072002300232072001500255072001500270100008100285245014700366250001800513264007500531300006700606336002600673337002600699338003600725347002400761490005700785505025400842520200601096650004703102650003103149650002303180650004703203650002603250650005103276650002303327650001903350650003503369700008403404710003403488773002603522776003603548776003603584830006403620856004603684912001403730912001503744999001703759978-3-031-42209-6DE-He21320251030111401.0cr nn 008mamaa240124s2024    sz |    s    |||| 0|eng d  a97830314220969978-3-031-42209-67 a10.1007/978-3-031-42209-62doi 4aQA76.9.D35 4aQ350-390 7aUMB2bicssc 7aGPF2bicssc 7aCOM0210002bisacsh 7aUMB2thema 7aGPF2thema1 aLee, Kent D.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96986510aData Structures and Algorithms with Pythonh[electronic resource] :bWith an Introduction to Multiprocessing /cby Kent D. Lee, Steve Hubbard.  a2nd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXVI, 398 p. 156 illus., 144 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aUndergraduate Topics in Computer Science,x2197-17810 a1. Python Programming 101 -- 2. Computational Complexity -- 3. Recursion -- 4. Sequences -- 5. Sets and Maps -- 6. Trees -- 7. Graphs -- 8. Membership Structures -- 9. Heaps -- 10. Balanced Binary Search Trees -- 11. B-Trees -- 12. Heuristic Search.  aThis 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 motivating examples—that bring meaning to the problems faced by computer programmers. The idea of computational complexity is introduced, demonstrating what can and cannot be computed efficiently at scale, helping programmers make informed judgements about the algorithms they use. The easy-to-read text assumes some basic experience in computer programming and familiarity in an object-oriented language, but not necessarily with Python. Topics and features: Includes introductory and advanced data structures and algorithms topics, with suggested chapter sequences for those respective courses Provides learning goals, review questions, and programming exercises in each chapter, as well as numerous examples Presents a primer on Python for those coming from a different language background Adds a new chapter on multiprocessing with Python using the DragonHPC multinode implementation of multiprocessing (includes a tutorial) Reviews the use of hashing in sets and maps, and examines binary search trees, tree traversals, and select graph algorithms Offers downloadable programs and supplementary files at an associated website to help students Students of computer science will find this clear and concise textbook invaluable for undergraduate courses on data structures and algorithms, at both introductory and advanced levels. The book is also suitable as a refresher guide for computer programmers starting new jobs working with Python. Dr. Kent D. Lee is a Professor Emeritus of Computer Science at Luther College, Decorah, Iowa, USA. He is the author of the successful Springer books, Python Programming Fundamentals, and Foundations of Programming Languages. Dr. Steve Hubbard is a Professor Emeritus of Mathematics and Computer Science at Luther College. 0aData structures (Computer science).966708 0aInformation theory.957783 0aAlgorithms.958677 0aPython (Computer program language).964608 0aComputer programming.14aData Structures and Information Theory.96670924aAlgorithms.95867724aPython.94755324aProgramming Techniques.9655171 aHubbard, Steve.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9698662 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303142208908iPrinted edition:z9783031422102 0aUndergraduate Topics in Computer Science,x2197-178196649440uhttps://doi.org/10.1007/978-3-031-42209-6  aZDB-2-SCS  aZDB-2-SXCS  c57292d57292