By Kent D. Lee, Steve Hubbard
This textbook explains the thoughts and methods required to write down courses which may deal with quite a lot of information successfully. Project-oriented and classroom-tested, the booklet provides a couple of vital algorithms supported via examples that convey intending to the issues confronted by means of laptop programmers. the belief of computational complexity is usually brought, demonstrating what can and can't be computed successfully in order that the programmer could make expert decisions concerning the algorithms they use. positive factors: comprises either introductory and complex information constructions and algorithms subject matters, with prompt bankruptcy sequences for these respective classes supplied within the preface; offers studying ambitions, overview questions and programming routines in each one bankruptcy, in addition to a number of illustrative examples; deals downloadable courses and supplementary documents at an linked web site, with teacher fabrics to be had from the writer; provides a primer on Python for these from a unique language background.
From the again Cover
This in actual fact established and simple to learn textbook explains the suggestions and strategies required to put in writing courses that could deal with quite a lot of info efficiently.
Project-oriented and classroom-tested, the e-book provides a couple of vital algorithms supported through motivating examples that deliver intending to the issues confronted by means of desktop programmers. the assumption of computational complexity can also be brought, demonstrating what can and can't be computed successfully in order that the programmer could make expert decisions in regards to the algorithms they use. The textual content assumes a few easy event in desktop programming and familiarity in an object-oriented language, yet no longer unavoidably with Python.
Topics and features:
Includes either introductory and complex information constructions and algorithms issues, with recommended bankruptcy sequences for these respective classes supplied within the preface
Provides studying targets, overview questions and programming routines in every one bankruptcy, in addition to a variety of illustrative examples
Offers downloadable courses and supplementary records at an linked site, with teacher fabrics on hand from the author
Presents a primer on Python for these coming from a special language background
Reviews using hashing in units and maps, in addition to an exam of binary seek timber and tree traversals, and fabric on intensity first seek of graphs
Discusses subject matters compatible for a complicated direction, corresponding to club constructions, lots, balanced binary seek timber, B-trees and heuristic search
Students of computing device technology will locate this transparent and concise textbook to be helpful for undergraduate classes on info buildings and algorithms, at either introductory and complicated degrees. The ebook can be compatible as a refresher advisor for computing device programmers beginning new jobs operating with Python.
About the Author
Dr. Kent D. Lee is Professor of computing device technological know-how at Luther university, Decorah, Iowa, united states. he's the writer of the profitable Springer textbook Python Programming basics and the drawing close Foundations of Programming Languages.
Dr. Steve Hubbard is Professor of arithmetic and machine technological know-how at Luther collage.
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Extra resources for Data Structures and Algorithms With Python
Xmin = 1000 xmax = 200000 17 18 19 20 21 # Record the list sizes in xList and the average access time within # a list that size in yList for 1000 retrievals. now() 38 39 40 41 42 43 44 45 46 47 for v in range(1000): # Find a random location within the list # and retrieve a value. Do a dummy operation # with that value to ensure it is really retrieved. now() 48 49 50 # The difference in time between start and end. deltaT = endtime - starttime 51 52 53 54 # Divide by 1000 for the average access time # But also multiply by 1000000 for microseconds.
2. 2 A Graphics Program 1 import turtle 2 3 4 5 # Command classes would be inserted here but are left out because they # were defined earlier in the chapter. 13 The Accumulator Pattern 6 7 23 # This is our PyList class. It holds a list of our graphics # commands. items + [item] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 # if we want to iterate over this sequence, we define the special method # called __iter__(self). TypeError: # ’PyList’ object is not iterable" if we try to write # for cmd in seq: # where seq is one of these sequences.
5. If you were to execute the code you wrote for answering the last three questions, what would the string referenced by text contain after executing these three lines of code? 6. How would you create a dictionary that maps “Kent” to “Denise” and maps “Steve” to “Lindy”? In these two cases “Kent” and “Steve” are the keys and “Denise” and “Lindy” are the values. 7. Consult Chap. 17. How would you map a key to a value as in the previous problem when the dictionary was first created as an empty dictionary?
Data Structures and Algorithms With Python by Kent D. Lee, Steve Hubbard