How to Think Like a Computer Scientist - Learning with Python, 2008a

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C.2 Recommended general computer science books 241 And here are some books that contain more material on the Python language: • Core Python Programming by Wesley Chun is a large book at about 750 pages. The first part of the book covers the basic Python language features. The second part provides an easy-paced introduction to more advanced topics including many of those mentioned above. • Python Essential Reference by David M. Beazley is a small book, but it is packed with information both on the language itself and the modules in the standard library. It is also very well indexed. • Python Pocket Reference by Mark Lutz really does fit in your pocket. Although not as extensive as Python Essential Reference it is a handy reference for the most commonly used functions and modules. Mark Lutz is also the author of Programming Python, one of the earliest (and largest) books on Python and not aimed at the beginning programmer. His later book Learning Python is smaller and more accessible. • Python Programming on Win32 by Mark Hammond and Andy Robinson is a “must have” for anyone seriously using Python to develop Windows applications. Among other things it covers the integration of Python and COM, builds a small application with wxPython, and even uses Python to script windows applications such as Word and Excel. C.2 Recommended general computer science books The following suggestions for further reading include many of the authors’ favorite books. They deal with good programming practices and computer science in general. • The Practice of Programming by Kernighan and Pike covers not only the design and coding of algorithms and data structures, but also debugging, testing and improving the performance of programs. The examples are mostly C++andJava,withnoneinPython. • The Elements of Java Style edited by Al Vermeulen is another small book that discusses some of the finer points of good programming, such as good use of naming conventions, comments, and even whitespace and indentation (somewhat of a nonissue in Python). The book also covers programming by contract, using assertions to catch errors by testing preconditions and postconditions, and proper programming with threads and their synchronization.
242 Recommendations for further reading • Programming Pearls by Jon Bentley is a classic book. It consists of case studies that originally appeared in the author’s column in the Communications of the ACM. The studies deal with tradeoffs in programming and why it is often an especially bad idea to run with your first idea for a program. The book is a bit older than those above (1986), so the examples are in older languages. There are lots of problems to solve, some with solutions and others with hints. This book was very popular and was followed by a second volume. • The New Turing Omnibus by A.K Dewdney provides a gentle introduction to 66 topics of computer science ranging from parallel computing to computer viruses, from cat scans to genetic algorithms. All of the topics are short and entertaining. An earlier book by Dewdney The Armchair Universe is a collection from his column Computer Recreations in Scientific American. Both books are a rich source of ideas for projects. • Turtles, Termites and Traffic Jams by Mitchel Resnick is about the power of decentralization and how complex behavior can arise from coordinated simple activity of a multitude of agents. It introduces the language StarLogo, which allows the user to write programs for the agents. Running the program demonstrates complex aggregate behavior, which is often counterintuitive. Many of the programs in the book were developed by students in middle school and high school. Similar programs could be written in Python using simple graphics and threads. • Gödel, Escher and Bach by Douglas Hofstadter. Put simply, if you found magic in recursion you will also find it in this bestselling book. One of Hofstadter’s themes involves “strange loops” where patterns evolve and ascend until they meet themselves again. It is Hofstadter’s contention that such “strange loops” are an essential part of what separates the animate from the inanimate. He demonstrates such patterns in the music of Bach, the pictures of Escher and Gödel’s incompleteness theorem.
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How to Think Like a Computer Scient
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How to Think Like a Computer Scient
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Foreword By David Beazley As an edu
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Preface By Jeff Elkner This book ow
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make instant changes whenever someo
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call (type) out its name.” Parame
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Contributor List To paraphrase the
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• Paul Sleigh found an error in C
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xvii • Roger Sperberg pointed out
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Contents Foreword v Preface vii Con
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Contents xxi 4.7 Nested conditional
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Contents xxiii 8.15 Matrices . . .
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Contents xxv 14.4 A more complicate
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Contents xxvii 19.4 Improved Linked
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Chapter 1 The way of the program Th
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1.2 What is a program? 3 $ python P
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1.3 What is debugging? 5 1.3.2 Runt
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1.4 Formal and natural languages 7
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1.6 Glossary 9 source code: A progr
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Chapter 2 Variables, expressions an
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2.3 Variable names and keywords 13
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2.4 Statements 15 >>> 76trombones =
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2.6 Operators and operands 17 2.6 O
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2.9 Composition 19 On one hand, thi
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2.11 Glossary 21 state diagram: A g
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Chapter 3 Functions 3.1 Function ca
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3.4 Math functions 25 >>> minute =
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3.6 Adding new functions 27 problem
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3.7 Definitions and use 29 3.7 Defi
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3.10 Variables and parameters are l
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3.12 Functions with results 33 The
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3.13 Glossary 35 local variable: A
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Chapter 4 Conditionals and recursio
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4.4 Conditional execution 39 In gen
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4.7 Nested conditionals 41 Each con
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4.9 Recursion 43 countdown expects
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4.11 Infinite recursion 45 4.11 Inf
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4.13 Glossary 47 block: A group of
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Chapter 5 Fruitful functions 5.1 Re
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5.2 Program development 51 avoid lo
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5.3 Composition 53 3. Once the prog
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5.5 More recursion 55 But the extra
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5.6 Leap of faith 57 __main__ facto
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5.8 Checking types 59 >>> factorial
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Chapter 6 Iteration 6.1 Multiple as
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6.2 The while statement 63 1. Evalu
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6.3 Tables 65 1.0 0.0 2.0 0.6931471
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6.5 Encapsulation and generalizatio
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6.7 Local variables 69 6.7 Local va
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6.9 Functions 71 the function is ca
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Chapter 7 Strings 7.1 A compound da
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7.3 Traversal and the for loop 75 T
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7.6 Strings are immutable 77 Other
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7.9 The string module 79 As an exer
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7.11 Glossary 81 There are other us
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Chapter 8 Lists A list is an ordere
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8.3 List length 85 If you try to re
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8.6 List operations 87 for VARIABLE
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8.9 List deletion 89 >>> list = [
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8.10 Objects and values 91 8.10 Obj
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8.13 List parameters 93 >>> b[0] =
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8.16 Strings and lists 95 might be
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Chapter 9 Tuples 9.1 Mutability and
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9.3 Tuples as return values 99 9.3
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9.6 Counting 101 >>> randomList(8)
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9.7 Many buckets 103 bucketWidth =
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9.9 Glossary 105 9.9 Glossary Assig
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Chapter 10 Dictionaries The compoun
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10.2 Dictionary methods 109 10.2 Di
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10.5 Hints 111 An alternative is to
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10.6 Long integers 113 Using this v
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10.8 Glossary 115 overflow: A numer
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Chapter 11 Files and exceptions Whi
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11.1 Text files 119 The following f
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11.2 Writing variables 121 the str
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11.3 Directories 123 11.3 Directori
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11.5 Exceptions 125 Or trying to op
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11.6 Glossary 127 format sequence:
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Chapter 12 Classes and objects 12.1
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12.3 Instances as arguments 131 con
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12.5 Rectangles 133 >>> p1 = Point(
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12.8 Copying 135 The variables dwid
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12.9 Glossary 137 12.9 Glossary cla
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Chapter 13 Classes and functions 13
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13.3 Modifiers 141 The output of th
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13.5 Prototype development versus p
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13.8 Glossary 145 Similarly, the te
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Chapter 14 Classes and methods 14.1
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14.3 Another example 149 class Time
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14.5 Optional arguments 151 if done
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14.7 Points revisited 153 >>> curre
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14.9 Polymorphism 155 def __mul__(s
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14.10 Glossary 157 Of course, we in
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Chapter 15 Sets of objects 15.1 Com
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15.3 Class attributes and the str m
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15.5 Decks 163 def __cmp__(self, ot
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15.7 Shuffling the deck 165 >>> dec
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15.9 Glossary 167 class Deck: ... d
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Chapter 16 Inheritance 16.1 Inherit
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16.3 Dealing cards 171 16.3 Dealing
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16.6 OldMaidHand class 173 class Ca
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16.7 OldMaidGame class 175 Hand fra
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16.7 OldMaidGame class 177 class Ol
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16.8 Glossary 179 Hand Allen picked
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Chapter 17 Linked lists 17.1 Embedd
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17.3 Lists as collections 183 node1
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17.5 Infinite lists 185 We invoke t
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17.8 Wrappers and helpers 187 def r
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17.10 Invariants 189 tail = self.ne
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Page 228 and 229: Chapter 19 Queues This chapter pres
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Page 232 and 233: 19.5 Priority queue 203 and it is m
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Page 236 and 237: Chapter 20 Trees Like linked lists,
Page 238 and 239: 20.2 Traversing trees 209 20.2 Trav
Page 240 and 241: 20.4 Tree traversal 211 def printTr
Page 242 and 243: 20.5 Building an expression tree 21
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Page 246 and 247: 20.7 The animal tree 217 Are you th
Page 248 and 249: 20.8 Glossary 219 def yes(ques): fr
Page 250 and 251: Appendix A Debugging Different kind
Page 252 and 253: A.2 Runtime errors 223 A.1.1 I can
Page 254 and 255: A.2 Runtime errors 225 If there is
Page 256 and 257: A.3 Semantic errors 227 A.3 Semanti
Page 258 and 259: A.3 Semantic errors 229 return self
Page 260 and 261: Appendix B Creating a new data type
Page 262 and 263: B.1 Fraction multiplication 233 >>>
Page 264 and 265: B.4 Comparing fractions 235 This re
Page 266 and 267: B.6 Glossary 237 reduce: To change
Page 268 and 269: Appendix C Recommendations for furt
Page 272 and 273: Index Make Way for Ducklings, 75 Py
Page 274 and 275: Index 245 element, 83, 96 embedded
Page 276 and 277: Index 247 well-formed, 189 list del
Page 278 and 279: Index 249 redundancy, 7 reference,
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