Programming Using Python
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1 Introduction to Computation and Programming Using Python Revised and Expanded Edition John V. Guttag The MIT Press Cambridge, Massachusetts London, England
2 CONTENTS PREFACE xiii ACKNOWLEDGMENTS xv 1 GETTING STARTED 1 2 INTRODUCTION TO PYTHON The Basic Elements of Python Objects, Expressions, and Numerical Types Variables and Assignment IDLE Branching Programs 2.3 Strings and Input Input Iteration 18 3 SOME SIMPLE NUMERICAL PROGRAMS Exhaustive Enumeration For Loops Approximate Solutions and Bisection Search A Few Words About Using Floats Newton-Raphson 32 4 FUNCTIONS, SCOPING, and ABSTRACTION Functions and Scoping Function Definitions Keyword Arguments and Default Values Scoping 4.2 Specifications Recursion Fibonacci Numbers Palindromes Global Variables Modules Files 53
3 5 STRUCTURED TYPES, MUTABILITY, AND HIGHER-ORDER FUNCTIONS Tuples Sequences and Multiple Assignment Lists and Mutability Cloning List Comprehension Functions as Objects Strings, Tuples, and Lists Dictionaries 67 6 TESTING AND DEBUGGING Testing Black-Box Testing Glass-Box Testing Conducting Tests Debugging Learning to Debug Designing the Experiment When the Going Gets Tough And When You Have Found "The" Bug 82 7 EXCEPTIONS AND ASSERTIONS Handling Exceptions Exceptions as a Control Flow Mechanism Assertions 90 8 CLASSES AND OBJECT-ORIENTED PROGRAMMING Abstract Data Types and Classes Designing Programs Using Abstract Data Types Using Classes to Keep Track of Students and Faculty Inheritance Multiple Levels of Inheritance The Substitution Principle Encapsulation and Information Hiding Generators Mortgages, an Extended Example 108
4 ix 9 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY Thinking About Computational Complexity Asymptotic Notation Some Important Complexity Classes Constant Complexity Logarithmic Complexity Linear Complexity Log-Linear Complexity Polynomial Complexity Exponential Complexity Comparisons of Complexity Classes SOME SIMPLE ALGORITHMS AND DATA STRUCTURES Search Algorithms Linear Search and Using Indirection to Access Elements Binary Search and Exploiting Assumptions Sorting Algorithms Merge Sort Exploiting Functions as Parameters Sorting in Python Hash Tables PLOTTING AND MORE ABOUT CLASSES Plotting Using PyLab Plotting Mortgages, an Extended Example STOCHASTIC PROGRAMS, PROBABILITY, AND STATISTICS Stochastic Programs Inferential Statistics and Simulation Distributions Normal Distributions and Confidence Levels Uniform Distributions Exponential and Geometric Distributions Benford's Distribution How Often Does the Better Team Win? Hashing and Collisions 177
5 X 13 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION The Drunkard's Walk Biased Random Walks Treacherous Fields MONTE CARLO SIMULATION Pascal's Problem Pass or Don't Pass? Using Table Lookup to Improve Performance Finding * Some Closing Remarks About Simulation Models UNDERSTANDING EXPERIMENTAL DATA The Behavior of Springs Using Linear Regression to Find a Fit The Behavior of Projectiles Coefficient of Determination Using a Computational Model Fitting Exponentially Distributed Data When Theory Is Missing LIES. DAMNED LIES, AND STATISTICS Garbage In Garbage Out (GIGO) Pictures Can Be Deceiving dim Hoc Ergo Propter Hoc Statistical Measures Don't Tell the Whole Story Sampling Bias Context Matters Beware of Extrapolation The Texas Sharpshooter Fallacy Percentages Can Confuse Just Beware KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS Knapsack Problems Greedy Algorithms An Optimal Solution to the 0/1 Knapsack Problem 238
6 17.2 Graph Optimization Problems Some Classic Graph-Theoretic Problems The Spread of Disease and Min Cut Shortest Path: Depth-First Search and Breadth-First Search DYNAMIC PROGRAMMING Fibonacci Sequences, Revisited Dynamic Programming and the 0/1 Knapsack Problem Dynamic Programming and Divide-and-Conquer A QUICK LOOK AT MACHINE LEARNING Feature Vectors Distance Metrics Clustering Types Example and Cluster K-means Clustering A Contrived Example A Less Contrived Example Wrapping Up 286 PYTHON 2.7 QUICK REFERENCE 287 INDEX 289
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