Introduction to computation and programming using Python:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA [u.a.]
MIT Press
2013
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| Ausgabe: | Spring 2013 edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | "This book is based on an MIT course that has been offered twice a year since 2006"--Preface Includes bibliographical references and index |
| Beschreibung: | xii, 268 pages Ill., graph. Darst. 28 cm |
| ISBN: | 0262519631 9780262519632 |
Internformat
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Datensatz im Suchindex
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| adam_text | Titel: Introduction to computation and programming using Python
Autor: Guttag, John V
Jahr: 2013
CONTENTS
PREFACE........................................................................................................ix
SPRING 2013 EDITION....................................................................................xi
ACKNOWLEDGMENTS....................................................................................xii
1 GETTING STARTED....................................................................................1
2 INTRODUCTION TO PYTHON......................................................................7
2.1 The Basic Element of Python.................................................................8
2.1.1 Objects, expressions, and numerical types.....................................9
2.1.2 Variables and assignment............................................................ 11
2.1.3 IDLE............................................................................................ 13
2.2 Branching Programs........................................................................... 13
2.3 Type Str and Input............................................................................. 16
2.3.1 Input............................................................................................ 18
2.4 Iteration.............................................................................................. 18
3 SOME SIMPLE NUMERICAL PROGRAMS..................................................21
3.1 Exhaustive enumeration.....................................................................21
3.2 For Loops............................................................................................23
3.3 Approximate Solutions and Bisection Search......................................25
3.4 A Few Words About Using Floats........................................................29
3.5 Newton-Raphson................................................................................31
4 FUNCTIONS AND ABSTRACTION BY SPECIFICATION...............................34
4.1 Functions and Scoping.......................................................................35
4.1.1 Function definitions.....................................................................35
4.1.2 Keyword Arguments and Default Values.......................................36
4.1.3 Scoping........................................................................................37
4.2 Specifications.....................................................................................40
4.3 Recursion...........................................................................................43
4.3.1 Fibonacci numbers.......................................................................45
4.3.2 Palindromes and divide-and-conquer............................................47
4.4 Global Variables.................................................................................49
4.5 Modules and Import............................................................................50
4.6 Files...................................................................................................52
5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS.. 55
5.1 Tuples................................................................................................55
5.1.1 Sequences and Multiple Assignment.............................................56
5.2 Lists and Mutability............................................................................57
5.2.1 Cloning........................................................................................62
5.2.2 List comprehension......................................................................62
5.3 Functions as Objects..........................................................................63
5.4 Strings, tuples, and lists.....................................................................64
5.5 Dictionaries........................................................................................66
6 TESTING AND DEBUGGING......................................................................70
6.1 Testing................................................................................................70
6.1.1 Black-box testing.........................................................................71
6.1.2 Glass-box testing..........................................................................73
6.1.3 Conducting Tests.........................................................................74
6.2 Debugging..........................................................................................76
6.2.1 Learning to Debug........................................................................78
6.2.2 Designing the Experiment............................................................79
6.2.3 When the Going Gets Tough.........................................................81
6.2.4 And When You Have Found The Bug..........................................82
7 EXCEPTIONS AND ASSERTIONS..............................................................83
7.1 Handling Exceptions...........................................................................83
7.2 Exceptions as a Control Flow Mechanism...........................................86
7.3 Assertions...........................................................................................88
8 CLASSES AND OBJECT-ORIENTED PROGRAMMING...............................90
8.1 Abstract Data Types and Classes........................................................90
8.1.1 Designing Programs Using Abstract Data Types............................94
8.1.2 Using Classes to Keep Track of Students and Faculty...................94
8.2 Inheritance.........................................................................................96
8.2.1 Multiple Levels of Inheritance.......................................................99
8.2.2 The Substitution Principle.......................................................... 100
8.3 Encapsulation and Information Hiding.............................................. 101
8.3.1 User-Defined Iterators................................................................ 104
8.4 Mortgages, an Extended Example..................................................... 106
9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY............ 111
9.1 Thinking About Computational Complexity....................................... Ill
9.2 Asymptotic Notation..........................................................................114
9.3 Some Important Complexity Classes................................................. 116
9.3.1 Constant complexity...................................................................116
9.3.2 Logarithmic complexity.............................................................. 116
9.3.3 Linear Complexity...................................................................... 117
9.3.4 Log-linear Complexity................................................................. 118
9.3.5 Polynomial Complexity............................................................... 118
9.3.6 Exponential Complexity.............................................................. 120
9.3.7 Comparisons of Complexity Classes............................................121
10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES......................... 123
10.1 Search Algorithms.......................................................................... 124
10.1.1 Linear Search and Using Indirection to Access Elements.......... 124
10.1.2 Binary Search and Exploiting Assumptions.............................. 126
10.2 Sorting Algorithms..........................................................................129
10.2.1 Merge Sort................................................................................ 130
10.2.2 Exploiting Functions as Parameters.......................................... 133
10.2.3 Sorting in Python..................................................................... 133
10.3 Hash Tables.................................................................................... 135
11 PLOTTING AND MORE ABOUT CLASSES................................................ 139
11.1 Plotting Using PyLab....................................................................... 139
11.2 Plotting Mortgages, an Extended Example....................................... 143
12 STOCHASTIC PROGRAMS, PROBABILITY, AND STATISTICS................... 150
12.1 Stochastic Programs....................................................................... 151
12.2 Inferential Statistics and Simulation............................................... 153
12.3 Distributions.................................................................................. 164
12.3.1 Normal Distributions and Confidence Levels............................. 166
12.3.2 Uniform Distributions.............................................................. 168
12.3.3 Exponential and Geometric Distributions................................. 168
12.3.4 Benford s Distribution.............................................................. 170
12.4 How Often Does the Better Team Win?............................................ 171
12.5 Hashing and Collisions................................................................... 174
13 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION................. 177
13.1 The Drunkard s Walk...................................................................... 177
13.2 Biased Random Walks.................................................................... 183
13.3 Treacherous Fields.......................................................................... 189
14 MONTE CARLO SIMULATION.................................................................. 191
14.1 Pascal s Problem............................................................................. 192
14.2 Pass or Don t Pass?......................................................................... 193
14.3 Using Table Lookup to Improve Performance................................... 196
14.4 Finding ji........................................................................................ 197
14.5 Some Closing Remarks About Simulation Models............................201
15 UNDERSTANDING EXPERIMENTAL DATA..............................................204
15.1 The Behavior of Springs..................................................................204
15.1.1 Using Linear Regression to Find a Fit.......................................207
15.2 The Behavior of Projectiles..............................................................211
15.2.1 Coefficient of Determination.....................................................212
15.2.2 Using a Compuational Model....................................................213
15.3 Not All Close Fits are Good Fits.......................................................214
15.4 Fitting Exponential Distributions....................................................219
15.5 When Theory is Missing..................................................................221
16 LIES, DAMNED LIES, AND STATISTICS..................................................222
16.1 Garbage In Garbage Out (GIGO)......................................................222
16.2 Pictures Can Be Deceiving..............................................................223
16.3 Cum Hoc Ergo Propter Hoc.............................................................224
16.4 Statistical Measures Don t Tell the Whole Story...............................225
16.5 Sampling Bias.................................................................................227
16.6 Context Matters..............................................................................228
16.7 Beware of Extrapolation..................................................................228
16.8 The Texas Sharpshooter Fallacy......................................................229
16.9 Percentages Can Confuse................................................................231
16.10 Just Beware..................................................................................232
17 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS..............................233
17.1 Knapsack Problems........................................................................233
17.1.1 Greedy Algorithms....................................................................234
17.1.2 Exponential Solution to the 0/1 Knapsack Problem..................237
17.2 Graph Optimization Problems.........................................................239
17.2.1 Some Classic Graph Theoretic Problems...................................243
17.2.2 The Spread of Disease and Min Cut..........................................244
17.2.3 Shortest Path, Depth-first search, and Breadth-first Search.....245
18 DYNAMIC PROGRAMMING.....................................................................250
18.1 Fibonacci Sequences, Revisited.......................................................250
18.2 Dynamic Programming and the 0/1 Knapsack Problem...................252
18.3 Dynamic Programming and Divide-and-Conquer.............................258
PYTHON 2.7 QUICK REFERENCE.................................................................259
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| spelling | Guttag, John V. Verfasser aut Introduction to computation and programming using Python John V. Guttag Spring 2013 edition Cambridge, MA [u.a.] MIT Press 2013 xii, 268 pages Ill., graph. Darst. 28 cm txt rdacontent n rdamedia nc rdacarrier "This book is based on an MIT course that has been offered twice a year since 2006"--Preface Includes bibliographical references and index Python (Computer program language) Python (Computer program language) / Study and teaching / Massachusetts / Cambridge Computer programming Informatik (DE-588)4026894-9 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Python Programmiersprache (DE-588)4434275-5 s DE-604 Informatik (DE-588)4026894-9 s HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025750326&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Guttag, John V. Introduction to computation and programming using Python Python (Computer program language) Python (Computer program language) / Study and teaching / Massachusetts / Cambridge Computer programming Informatik (DE-588)4026894-9 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4026894-9 (DE-588)4434275-5 (DE-588)4123623-3 |
| title | Introduction to computation and programming using Python |
| title_auth | Introduction to computation and programming using Python |
| title_exact_search | Introduction to computation and programming using Python |
| title_full | Introduction to computation and programming using Python John V. Guttag |
| title_fullStr | Introduction to computation and programming using Python John V. Guttag |
| title_full_unstemmed | Introduction to computation and programming using Python John V. Guttag |
| title_short | Introduction to computation and programming using Python |
| title_sort | introduction to computation and programming using python |
| topic | Python (Computer program language) Python (Computer program language) / Study and teaching / Massachusetts / Cambridge Computer programming Informatik (DE-588)4026894-9 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Python (Computer program language) Python (Computer program language) / Study and teaching / Massachusetts / Cambridge Computer programming Informatik Python Programmiersprache Lehrbuch |
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