Introduction to computation and programming using Python: with application to understanding data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, MA
The MIT Press
[2016]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xv, 447 Seiten Illustrationen |
| ISBN: | 9780262529624 |
Internformat
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| adam_text | CONTENTS PREFACE..................................................................................................................... xiii ACKNOWLEDGMENTS..........................................................................................xvii 1 GETTING STARTED................................................................................................ 1 2 INTRODUCTION TO PYTHON............................................................................7 2.1 The Basic Elements of Python...................................................................9 2.1.1 Objects, Expressions, and Numerical Types....................................... 9 2.1.2 Variables and Assignment................................................................ 12 2.1.3 Python IDE’s.....................................................................................14 2.2 Branching Programs................................................................................ 15 2.3 Strings and Input.....................................................................................18 2.3.1 Input..................................................................................................20 2.3.2 A Digression About Character Encoding.........................................21 2.4 Iteration...................................................................................................22 3 SOME SIMPLE NUMERICAL PROGRAMS...................................................... 25 3.1 3.2 3.3 3.4 3.5 Exhaustive Enumeration.........................................................................25
For Loops.................................................................................................27 Approximate Solutions and Bisection Search.........................................30 A Few Words About Using Floats........................................................... 34 Newton-Raphson.....................................................................................37 4 FUNCTIONS, SCOPING, AND ABSTRACTION.............................................39 4.1 Functions and Scoping............................................................................40 4.1.1 Function Definitions........................................................................ 40 4.1.2 Keyword Arguments and Default Values..........................................42 4.1.3 Scoping..............................................................................................43 4.2 Specifications........................................................................................... 47 4.3 Recursion.................................................................................................50 4.3.1 Fibonacci Numbers......................................................................... 52 4.3.2 Palindromes...................................................................................... 54 4.4 Global Variables...................................................................................... 57 4.5 Modules.................................................................................................. 59 4.6
Files......................................................................................................... 61
INTRODUCTION TO COMPUTATION AND PROGRAMMING USING PYTHON viii 5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS............................................................................................................ 65 5.1 Tuples...................................................................................................... 65 5.1.1 Sequences and Multiple Assignment................................................ 67 5.2 Ranges..................................................................................................... 67 5.3 Lists and Mutability.................................................................................68 5.3.1 Cloning............................................................................................. 73 5.3.2 List Comprehension......................................................................... 74 5.4 Functions as Objects................................................................................ 75 5.5 Strings, Tuples, Ranges, and Lists........................................................... 77 5.6 Dictionaries............................................................................................. 79 6 TESTING AND DEBUGGING..............................................................................85 6.1 Testing..................................................................................................... 86 6.1.1 Black-Box Testing............................................................................. 87 6.1.2 Glass-box
Testing.............................................................................. 88 6.1.3 Conducting Tests.............................................................................. 90 6.2 Debugging...............................................................................................92 6.2.1 Learning to Debug............................................................................ 94 6.2.2 Designing the Experiment................................................................ 95 6.2.3 When the Going Gets Tough............................................................ 98 6.2.4 When You Have Found “The” Bug...................................................99 7 EXCEPTIONS AND ASSERTIONS................................................................... 101 7.1 7.2 7.3 Handling Exceptions............................................................................. 101 Exceptions as a Control Flow Mechanism.............................................105 Assertions.............................................................................................. 108 8 CLASSES AND OBJECT-ORIENTED PROGRAMMING.............................109 8.1 Abstract Data Types and Classes...........................................................109 8.1.1 Designing Programs Using Abstract Data Types............................114 8.1.2 Using Classes to Keep Track of Students and Faculty.................... 115 8.2 Inheritance............................................................................................. 118 8.2.1 Multiple Levels of
Inheritance.........................................................121 8.2.2 The Substitution Principle.............................................................. 123 8.3 Encapsulation and Information Hiding................................................ 123 8.3.1 Generators...................................................................................... 128 8.4 Mortgages, an Extended Example..........................................................130 9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY.. 135 9.1 9.2 Thinking About Computational Complexity........................................ 135 Asymptotic Notation............................................................................. 139
CONTENTS ix 9.3Some Important Complexity Classes.........................................................141 9.3.1 Constant Complexity......................................................................141 9.3.2 Logarithmic Complexity................................................................. 141 9.3.3 Linear Complexity...........................................................................142 9.3.4 Log-Linear Complexity................................................................... 144 9.3.5 Polynomial Complexity.................................................................. 144 9.3.6 Exponential Complexity................................................................. 145 9.3.7 Comparisons of Complexity Classes...............................................147 10 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES......................151 10.1 Search Algorithms................................................................................. 152 10.1.1 Linear Search and Using Indirection to Access Elements..............153 10.1.2 Binary Search and Exploiting Assumptions................................... 154 10.2 Sorting Algorithms................................................................................ 158 10.2.1 Merge Sort.......................................................................................159 10.2.2 Exploiting Functions as Parameters............................................... 162 10.2.3 Sorting in Python............................................................................ 162 10.3 Hash
Tables........................................................................................... 164 11 PLOTTING AND MORE ABOUT CLASSES................................................... 169 11.1 Plotting Using PyLab............................................................................. 169 11.2 Plotting Mortgages, an Extended Example............................................175 12 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS...........................183 12.1 Knapsack Problems............................................................................... 184 12.1.1 Greedy Algorithms..........................................................................184 12.1.2 An Optimal Solution to the 0/1 Knapsack Problem...................... 188 12.2 Graph Optimization Problems.............................................................. 190 12.2.1 Some Classic Graph-Theoretic Problems....................................... 195 12.2.2 Shortest Path: Depth-First Search and Breadth-First Search........ 196 13 DYNAMIC PROGRAMMING............................................................................203 13.1 Fibonacci Sequences, Revisited............................................................. 203 13.2 Dynamic Programming and the 0/1Knapsack Problem..................... 205 13.3 Dynamic Programming and Divide-and-Conquer.............................. 213 14 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION........ 215 14.1 14.2 14.3 14.4 Random Walks...................................................................................... 216 The Drunkard’s
Walk............................................................................ 217 Biased Random Walks........................................................................... 224 Treacherous Fields.................................................................................231
x INTRODUCTION TO COMPUTATION AND PROGRAMMING USING PYTHON 15 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS.... 235 15.1 Stochastic Programs.............................................................................. 236 15.2 Calculating Simple Probabilities........................................................... 238 15.3 Inferential Statistics............................................................................... 239 15.4 Distributions......................................................................................... 254 15.4.1 Probability Distributions................................................................ 256 15.4.2 Normal Distributions......................................................................258 15.4.3 Continuous and Discrete Uniform Distributions.......................... 263 15.4.4 Binomial and Multinomial Distributions...................................... 264 15.4.5 Exponential and Geometric Distributions..................................... 265 15.4.6 Benfords Distribution.....................................................................269 15.5 Hashing and Collisions......................................................................... 269 15.6 How Often Does the Better Team Win?................................................272 16 MONTE CARLO SIMULATION...................................................................... 275 16.1 16.2 16.3 16.4 16.5 Pascal’s Problem.....................................................................................276 Pass or Don’t
Pass?.................................................................................277 Using Table Lookup to Improve Performance......................................282 Finding π................................................................................................283 Some Closing Remarks About Simulation Models............................... 288 17 SAMPLING AND CONFIDENCE INTERVALS..............................................291 17.1 Sampling the Boston Marathon............................................................ 292 17.2 The Central Limit Theorem..................................................................298 17.3 Standard Error of the Mean..................................................................302 18 UNDERSTANDING EXPERIMENTAL DATA................................................305 18.1 The Behavior of Springs........................................................................ 305 18.1.1 Using Linear Regression to Find a Fit............................................309 18.2 The Behavior of Projectiles...................................................................314 18.2.1 Coefficient of Determination......................................................... 317 18.2.2 Using a Computational Model........................................................319 18.3 Fitting Exponentially Distributed Data................................................. 320 18.4 When Theory Is Missing........................................................................324 19 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING........................ 327
19.1 19.2 19.3 19.4 19.5 19.6 Checking Significance........................................................................... 328 Beware of P-values.................................................................................334 One-tail and One-sample Tests............................................................. 336 Significant or Not?.................................................................................338 Which N?...............................................................................................340 Multiple Hypotheses.............................................................................. 342
CONTENTS xi 20 CONDITIONAL PROBABILITY AND BAYESIAN STATISTICS................ 345 20.1 Conditional Probabilities...........................................................................346 20.2 Bayes’Theorem.......................................................................................... 348 20.3 Bayesian Updating......................................................................................350 21 LIES, DAMNED LIES, AND STATISTICS............................................................355 21.1 Garbage In Garbage Out (GIGO).............................................................355 21.2 Tests Are Imperfect.................................................................................. 356 21.3 Pictures Can Be Deceiving.........................................................................357 21.4 Cum Hoc Ergo Propter Hoc......................................................................359 21.5 Statistical Measures Don’t Tell the Whole Story..................................... 361 21.6 Sampling Bias............................................................................................. 362 21.7 Context Matters......................................................................................... 363 21.8 Beware of Extrapolation.............................................................................364 21.9 The Texas Sharpshooter Fallacy................................................................364 21.10 Percentages Can Confuse...........................................................................367 21.11 Statistically
Significant Differences Can Be Insignificant...................... 368 21.12 The Regressive Fallacy...............................................................................369 21.13 Just Beware................................................................................................. 370 22 A QUICK LOOK AT MACHINE LEARNING.................................................... 371 22.1 Feature Vectors.......................................................................................... 374 22.2 Distance Metrics........................................................................................ 377 23 CLUSTERING............................................................................................................... 383 23.1 23.2 23.3 23.4 Class Cluster............................................................................................... 385 К-means Clustering....................................................................................387 A Contrived Example.................................................................................390 A Less Contrived Example.........................................................................395 24 CLASSIFICATION METHODS............................................................................... 403 24.1 24.2 24.3 24.4 24.5 24.6 Evaluating Classifiers................................................................................ 403 Predicting the Gender of Runners............................................................408 К-nearest
Neighbors.................................................................................. 408 Regression-based Classifiers......................................................................415 Surviving the Titanic................................................................................. 425 Wrapping Up.............................................................................................. 430 PYTHON 3.5 QUICK REFERENCE............................................................................. 431 INDEX................................................................................................................................... 435
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| title | Introduction to computation and programming using Python with application to understanding data |
| title_auth | Introduction to computation and programming using Python with application to understanding data |
| title_exact_search | Introduction to computation and programming using Python with application to understanding data |
| title_full | Introduction to computation and programming using Python with application to understanding data John V. Guttag |
| title_fullStr | Introduction to computation and programming using Python with application to understanding data John V. Guttag |
| title_full_unstemmed | Introduction to computation and programming using Python with application to understanding data John V. Guttag |
| title_short | Introduction to computation and programming using Python |
| title_sort | introduction to computation and programming using python with application to understanding data |
| title_sub | with application to understanding data |
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| topic_facet | Python (Computer program language) Textbooks Computer programming Textbooks Python Programmiersprache |
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Inhaltsverzeichnis
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2000 ST 250 P99 G985(2) |
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