I Introduction |
1 Overview |
1.1 What This Book is About |
1.2 The Values That Drive This Book |
1.3 Our Perspective on Data |
1.4 What Makes This Book Unique |
1.5 Who This Book is For |
1.6 The Structure of This Book |
1.7 Organization of the Material |
1.8 Our Programming Language Choice |
1.9 Sending Feedback, Errors, and Comments |
2 Acknowledgments |
|
II Foundations |
3 Getting Started |
3.1 Motivating Example: Flags |
3.2 Numbers |
3.3 Expressions |
3.4 Terminology |
3.5 Strings |
3.6 Images |
3.6.1 Combining Images |
3.6.2 Making a Flag |
3.7 Stepping Back: Types, Errors, and Documentation |
3.7.1 Types and Contracts |
3.7.2 Format and Notation Errors |
3.7.3 Finding Other
Functions: Documentation |
4 Naming Values |
4.1 The Definitions Pane |
4.2 Naming Values |
4.2.1 Names Versus Strings |
4.2.2 Expressions versus
Statements |
4.3 The Program Directory |
4.3.1 Understanding the Run Button |
4.4 Using Names to Streamline Building Images |
5 From Repeated Expressions to Functions |
5.1 Example: Similar Flags |
5.2 Defining Functions |
5.2.1 How Functions Evaluate |
5.2.2 Type Annotations |
5.2.3 Documentation |
5.3 Functions Practice: Moon Weight |
5.4 Documenting Functions with Examples |
5.5 Functions Practice: Cost of pens |
5.6 Recap: Defining Functions |
6 Conditionals and Booleans |
6.1 Motivating Example: Shipping Costs |
6.2 Conditionals: Computations with Decisions |
6.3 Booleans |
6.3.1 Other Boolean Operations |
6.3.2 Combining Booleans |
6.4 Asking Multiple Questions |
6.5 Evaluating by Reducing Expressions |
6.6 Composing Functions |
6.6.1 How Function Compositions Evaluate |
6.6.2 Function Composition and the Directory |
6.7 Nested Conditionals |
6.8 Recap: Booleans and Conditionals |
7 Introduction to Tabular Data |
7.1 Creating Tabular Data |
7.2 Extracting Rows and Cell Values |
7.3 Functions over Rows |
7.4 Processing Rows |
7.4.1 Finding Rows |
7.4.2 Ordering Rows |
7.4.3 Adding New Columns |
7.4.4 Calculating New Column Values |
7.5 Examples for Table-Producing Functions |
8 Processing Tables |
8.1 Cleaning Data Tables |
8.1.1 Loading Data Tables |
8.1.2 Dealing with Missing Entries |
8.1.3 Normalizing Data |
8.1.4 Normalization, Systematically |
8.1.4.1 Using Programs to Detect Data Errors |
8.2 Task Plans |
8.3 Preparing Data Tables |
8.3.1 Creating bins |
8.3.2 Splitting Columns |
8.4 Managing and Naming Data Tables |
8.5 Visualizations and Plots |
8.6 Summary: Managing a Data Analysis |
9 From Tables to Lists |
9.1 Basic Statistical Questions |
9.2 Extracting a Column from a Table |
9.3 Understanding Lists |
9.3.1 Lists as Anonymous Data |
9.3.2 Creating Literal Lists |
9.4 Operating on Lists |
9.4.1 Built-In Operations on Lists of Numbers |
9.4.2 Built-In Operations on Lists in General |
9.4.3 An Aside on Naming Conventions |
9.4.4 Getting Elements By Position |
9.4.5 Transforming Lists |
9.4.6 Recap: Summary of List Operations |
9.5 Lambda: Anonymous Functions |
9.6 Combining Lists and Tables |
10 Processing Lists |
10.1 Making Lists and Taking Them Apart |
10.2 Some Example Exercises |
10.3 Structural Problems with Scalar Answers |
10.3.1 my-len: Examples |
10.3.2 my-sum: Examples |
10.3.3 From Examples to Code |
10.4 Structural Problems that Transform Lists |
10.4.1 my-doubles: Examples and Code |
10.4.2 my-str-len: Examples and Code |
10.5 Structural Problems that Select from Lists |
10.5.1 my-pos-nums: Examples and Code |
10.5.2 my-alternating:
Examples and Code |
10.6 Structural Problems Over Relaxed Domains |
10.6.1 my-max: Examples |
10.6.2 my-max: From Examples to Code |
10.7 More Structural Problems with Scalar Answers |
10.7.1 my-avg: Examples |
10.8 Structural Problems with Accumulators |
10.8.1 my-running-sum: First Attempt |
10.8.2 my-running-sum: Examples and Code |
10.8.3 my-alternating: Examples and Code |
10.9 Dealing with Multiple Answers |
10.9.1 uniq: Problem Setup |
10.9.2 uniq: Examples |
10.9.3 uniq: Code |
10.9.4 uniq: Reducing Computation |
10.9.5 uniq: Example and Code Variations |
10.9.6 uniq: Why Produce a List? |
10.10 Monomorphic Lists and Polymorphic Types |
11 Introduction to Structured Data |
11.1 Understanding the Kinds of Compound Data |
11.1.1 A First Peek at Structured Data |
11.1.2 A First Peek at Conditional Data |
11.2 Defining and Creating Structured and Conditional Data |
11.2.1 Defining and Creating Structured Data |
11.2.2 Annotations for Structured Data |
11.2.3 Defining and Creating Conditional Data |
11.3 Programming with Structured and Conditional Data |
11.3.1 Extracting Fields from Structured Data |
11.3.2 Telling Apart Variants of Conditional Data |
11.3.3 Processing Fields of Variants |
12 Collections of Structured Data |
12.1 Lists as Collective Data |
12.2 Sets as Collective Data |
12.2.1 Picking Elements from Sets |
12.2.2 Computing with Sets |
12.3 Combining Structured and Collective Data |
12.4 Data Design Problem: Representing Quizzes |
13 Recursive Data |
13.1 Functions to Process Recursive Data |
13.2 A Template for Processing Recursive Data |
14 Trees |
14.1 Data Design Problem – Ancestry Data |
14.1.1 Computing Genetic Parents from an Ancestry Table |
14.1.2 Computing Grandparents from an Ancestry Table |
14.1.3 Creating a Datatype for Ancestor Trees |
14.2 Programs to Process Ancestor Trees |
14.3 Summarizing How to Approach Tree Problems |
14.4 Study Questions |
15 Functions as Data |
15.1 A Little Calculus |
15.2 A Helpful Shorthand for Anonymous Functions |
15.3 Streams From Functions |
15.4 Combining Forces: Streams of Derivatives |
16 Interactive Games as Reactive Systems |
16.1 About Reactive Animations |
16.2 Preliminaries |
16.3 Version: Airplane Moving Across the Screen |
16.3.1 Updating the World State |
16.3.2 Displaying the World State |
16.3.3 Observing Time (and Combining the Pieces) |
16.4 Version: Wrapping Around |
16.5 Version: Descending |
16.5.1 Moving the Airplane |
16.5.2 Drawing the Scene |
16.5.3 Finishing Touches |
16.6 Version: Responding to Keystrokes |
16.7 Version: Landing |
16.8 Version: A Fixed Balloon |
16.9 Version: Keep Your Eye on the Tank |
16.10 Version: The Balloon Moves, Too |
16.11 Version: One, Two, ..., Ninety-Nine Luftballons! |
17 Examples, Testing, and Program Checking |
17.1 From Examples to Tests |
17.2 More Refined Comparisons |
17.3 When Tests Fail |
17.4 Oracles for Testing |
|
III Algorithms |
18 Predicting Growth |
18.1 A Little (True) Story |
18.2 The Analytical Idea |
18.3 A Cost Model for Pyret Running Time |
18.4 The Size of the Input |
18.5 The Tabular Method for Singly-Structurally-Recursive Functions |
18.6 Creating Recurrences |
18.7 A Notation for Functions |
18.8 Comparing Functions |
18.9 Combining Big-Oh Without Woe |
18.10 Solving Recurrences |
19 Sets Appeal |
19.1 Representing Sets by Lists |
19.1.1 Representation Choices |
19.1.2 Time Complexity |
19.1.3 Choosing Between Representations |
19.1.4 Other Operations |
19.2 Making Sets Grow on Trees |
19.2.1 Converting Values to Ordered Values |
19.2.2 Using Binary Trees |
19.2.3 A Fine Balance: Tree Surgery |
19.2.3.1 Left-Left Case |
19.2.3.2 Left-Right Case |
19.2.3.3 Any Other Cases? |
20 Halloween Analysis |
20.1 A First Example |
20.2 The New Form of Analysis |
20.3 An Example: Queues from Lists |
20.3.1 List Representations |
20.3.2 A First Analysis |
20.3.3 More Liberal Sequences of Operations |
20.3.4 A Second Analysis |
20.3.5 Amortization Versus Individual Operations |
20.4 Reading More |
21 Sharing and Equality |
21.1 Re-Examining Equality |
21.2 The Cost of Evaluating References |
21.3 Notations for Equality |
21.4 On the Internet, Nobody Knows You’re a DAG |
21.5 It’s Always Been a DAG |
21.6 From Acyclicity to Cycles |
22 Graphs |
22.1 Understanding Graphs |
22.2 Representations |
22.2.1 Links by Name |
22.2.2 Links by Indices |
22.2.3 A List of Edges |
22.2.4 Abstracting Representations |
22.3 Measuring Complexity for Graphs |
22.4 Reachability |
22.4.1 Simple Recursion |
22.4.2 Cleaning up the Loop |
22.4.3 Traversal with Memory |
22.4.4 A Better Interface |
22.5 Depth- and Breadth-First Traversals |
22.6 Graphs With Weighted Edges |
22.7 Shortest (or Lightest) Paths |
22.8 Moravian Spanning Trees |
22.8.1 The Problem |
22.8.2 A Greedy Solution |
22.8.3 Another Greedy Solution |
22.8.4 A Third Solution |
22.8.5 Checking Component Connectedness |
|
IV From Pyret to Python |
23 From Pyret to Python |
23.1 Expressions, Functions, and Types |
23.2 Returning Values from Functions |
23.3 Examples and Test Cases |
23.4 An Aside on Numbers |
23.5 Conditionals |
23.6 Creating and Processing
Lists |
23.6.1 Filters, Maps, and Friends |
23.7 Data with Components |
23.7.1 Accessing Fields within Dataclasses |
23.8 Traversing Lists |
23.8.1 Introducing For Loops |
23.8.1.1 An Aside on Order of Processing List Elements |
23.8.2 Using For Loops in Functions that Produce Lists |
23.8.3 Summary: The List-Processing Template for Python |
|
V Programming with State |
24 Modifying Structured Data |
24.1 Modifying Fields of Structured Data |
24.2 Modifications to Shared Data |
24.3 Understanding Memory |
24.4 Variables and Equality |
24.5 Basic Data in Memory |
25 Modifying Variables |
25.1 Modifying Variables in Memory |
25.2 Modifying Variables Associated with Lists |
25.3 Writing Functions that Modify Variables |
25.3.1 The global annotation |
25.4 Testing Functions that Modify global Variables |
25.4.1 The Internal Structure of a Test Function |
25.4.2 Takeaways on Testing Modifications |
26 Revisiting Lists and Variables |
26.1 Sharing List Updates |
26.1.1 Operations that Mutate Lists |
26.2 Lists in Memory |
26.3 Practice: Data for Shared Bank Accounts |
26.4 Circular References |
26.4.1 Testing Circular Data |
26.4.2 Revisiting Variables: A Function to Create Accounts for New Customers |
26.5 The Many Roles of Variables |
26.6 Managing All Accounts |
27 Hashtables and Dictionaries |
27.1 Searching by Criteria Other than Keys |
27.2 Dictionaries with More Complex Values |
27.3 Using Structured Data as Keys |
|
VI Advanced Topics |
28 Algorithms That Exploit State |
28.1 Disjoint Sets Redux |
28.1.1 Optimizations |
28.1.2 Analysis |
28.2 Set Membership by Hashing Redux |
28.2.1 Improving Access Time |
28.2.2 Better Hashing |
28.2.3 Bloom Filters |
28.3 Avoiding Recomputation by Remembering Answers |
28.3.1 An Interesting Numeric Sequence |
28.3.1.1 Using State to Remember Past Answers |
28.3.1.2 From a Tree of Computation to a DAG |
28.3.1.3 The Complexity of Numbers |
28.3.1.4 Abstracting Memoization |
28.3.2 Edit-Distance for Spelling Correction |
28.3.3 Nature as a Fat-Fingered Typist |
28.3.4 Dynamic Programming |
28.3.4.1 Catalan Numbers with Dynamic Programming |
28.3.4.2 Levenshtein Distance and Dynamic Programming |
28.3.5 Contrasting Memoization and Dynamic Programming |
|
VII Appendices |
29 Pyret for Racketeers and Schemers |
29.1 Numbers, Strings, and Booleans |
29.2 Infix Expressions |
29.3 Function Definition and Application |
29.4 Tests |
29.5 Variable Names |
29.6 Data Definitions |
29.7 Conditionals |
29.8 Lists |
29.9 First-Class Functions |
29.10 Annotations |
29.11 What Else? |
30 Pyret vs. Python |
31 Comparing This Book to HtDP |
32 Release Notes |
33 Glossary |