Contents

  1. Preface xix

  2. Before You Begin xlv

  1. 1 Introduction to Computers and Python 1

    1. 1.1 Introduction 2

    2. 1.2 Hardware and Software 3

      1. 1.2.1 Moore’s Law 4

      2. 1.2.2 Computer Organization 4

    3. 1.3 Data Hierarchy 6

    4. 1.4 Machine Languages, Assembly Languages and High-Level Languages 9

    5. 1.5 Introduction to Object Technology 10

    6. 1.6 Operating Systems 13

    7. 1.7 Python 16

    8. 1.8 It’s the Libraries! 18

      1. 1.8.1 Python Standard Library 18

      2. 1.8.2 Data-Science Libraries 18

    9. 1.9 Other Popular Programming Languages 20

    10. 1.10 Test-Drive: Using IPython and Jupyter Notebooks 21

      1. 1.10.1 Using IPython Interactive Mode as a Calculator 21

      2. 1.10.2 Executing a Python Program Using the IPython Interpreter 23

      3. 1.10.3 Writing and Executing Code in a Jupyter Notebook 24

    11. 1.11 Internet and World Wide Web 29

      1. 1.11.1 Internet: A Network of Networks 29

      2. 1.11.2 World Wide Web: Making the Internet User-Friendly 30

      3. 1.11.3 The Cloud 30

      4. 1.11.4 Internet of Things 31

    12. 1.12 Software Technologies 32

    13. 1.13 How Big Is Big Data? 33

      1. 1.13.1 Big Data Analytics 38

      2. 1.13.2 Data Science and Big Data Are Making a Difference: Use Cases 39

    14. 1.14 Intro to Data Science: Case Study—A Big-Data Mobile Application 40

  2. 2 Introduction to Python Programming 49

    1. 2.1 Introduction 50

    2. 2.2 Variables and Assignment Statements 50

    3. 2.3 Arithmetic 52

    4. 2.4 Function print and an Intro to Single- and Double-Quoted Strings 56

    5. 2.5 Triple-Quoted Strings 58

    6. 2.6 Getting Input from the User 59

    7. 2.7 Decision Making: The if Statement and Comparison Operators 61

    8. 2.8 Objects and Dynamic Typing 66

    9. 2.9 Intro to Data Science: Basic Descriptive Statistics 68

    10. 2.10 Wrap-Up 70

  3. 3 Control Statements and Program Development 73

    1. 3.1 Introduction 74

    2. 3.2 Algorithms 74

    3. 3.3 Pseudocode 75

    4. 3.4 Control Statements 75

    5. 3.5 if Statement 78

    6. 3.6 if…else and if…elif…else Statements 80

    7. 3.7 while Statement 85

    8. 3.8 for Statement 86

      1. 3.8.1 Iterables, Lists and Iterators 88

      2. 3.8.2 Built-In range Function 88

    9. 3.9 Augmented Assignments 89

    10. 3.10 Program Development: Sequence-Controlled Repetition 90

      1. 3.10.1 Requirements Statement 90

      2. 3.10.2 Pseudocode for the Algorithm 90

      3. 3.10.3 Coding the Algorithm in Python 91

      4. 3.10.4 Introduction to Formatted Strings 92

    11. 3.11 Program Development: Sentinel-Controlled Repetition 93

    12. 3.12 Program Development: Nested Control Statements 97

    13. 3.13 Built-In Function range: A Deeper Look 101

    14. 3.14 Using Type Decimal for Monetary Amounts 102

    15. 3.15 break and continue Statements 105

    16. 3.16 Boolean Operators and, or and not 106

    17. 3.17 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 109

    18. 3.18 Wrap-Up 111

  4. 4 Functions 119

    1. 4.1 Introduction 120

    2. 4.2 Defining Functions 120

    3. 4.3 Functions with Multiple Parameters 123

    4. 4.4 Random-Number Generation 125

    5. 4.5 Case Study: A Game of Chance 128

    6. 4.6 Python Standard Library 131

    7. 4.7 math Module Functions 132

    8. 4.8 Using IPython Tab Completion for Discovery 133

    9. 4.9 Default Parameter Values 135

    10. 4.10 Keyword Arguments 136

    11. 4.11 Arbitrary Argument Lists 136

    12. 4.12 Methods: Functions That Belong to Objects 138

    13. 4.13 Scope Rules 138

    14. 4.14 import: A Deeper Look 140

    15. 4.15 Passing Arguments to Functions: A Deeper Look 142

    16. 4.16 Function-Call Stack 145

    17. 4.17 Functional-Style Programming 146

    18. 4.18 Intro to Data Science: Measures of Dispersion 148

    19. 4.19 Wrap-Up 150

  5. 5 Sequences: Lists and Tuples 155

    1. 5.1 Introduction 156

    2. 5.2 Lists 156

    3. 5.3 Tuples 161

    4. 5.4 Unpacking Sequences 163

    5. 5.5 Sequence Slicing 166

    6. 5.6 del Statement 169

    7. 5.7 Passing Lists to Functions 171

    8. 5.8 Sorting Lists 172

    9. 5.9 Searching Sequences 174

    10. 5.10 Other List Methods 176

    11. 5.11 Simulating Stacks with Lists 178

    12. 5.12 List Comprehensions 179

    13. 5.13 Generator Expressions 181

    14. 5.14 Filter, Map and Reduce 182

    15. 5.15 Other Sequence Processing Functions 185

    16. 5.16 Two-Dimensional Lists 187

    17. 5.17 Intro to Data Science: Simulation and Static Visualizations 191

      1. 5.17.1 Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls 191

      2. 5.17.2 Visualizing Die-Roll Frequencies and Percentages 193

    18. 5.18 Wrap-Up 199

  6. 6 Dictionaries and Sets 209

    1. 6.1 Introduction 210

    2. 6.2 Dictionaries 210

      1. 6.2.1 Creating a Dictionary 210

      2. 6.2.2 Iterating through a Dictionary 212

      3. 6.2.3 Basic Dictionary Operations 212

      4. 6.2.4 Dictionary Methods keys and values 214

      5. 6.2.5 Dictionary Comparisons 216

      6. 6.2.6 Example: Dictionary of Student Grades 217

      7. 6.2.7 Example: Word Counts 218

      8. 6.2.8 Dictionary Method update 220

      9. 6.2.9 Dictionary Comprehensions 220

    3. 6.3 Sets 221

      1. 6.3.1 Comparing Sets 223

      2. 6.3.2 Mathematical Set Operations 225

      3. 6.3.3 Mutable Set Operators and Methods 226

      4. 6.3.4 Set Comprehensions 228

    4. 6.4 Intro to Data Science: Dynamic Visualizations 228

      1. 6.4.1 How Dynamic Visualization Works 228

      2. 6.4.2 Implementing a Dynamic Visualization 231

    5. 6.5 Wrap-Up 234

  7. 7 Array-Oriented Programming with NumPy 239

    1. 7.1 Introduction 240

    2. 7.2 Creating arrays from Existing Data 241

    3. 7.3 array Attributes 242

    4. 7.4 Filling arrays with Specific Values 244

    5. 7.5 Creating arrays from Ranges 244

    6. 7.6 List vs. array Performance: Introducing %timeit 246

    7. 7.7 array Operators 248

    8. 7.8 NumPy Calculation Methods 250

    9. 7.9 Universal Functions 252

    10. 7.10 Indexing and Slicing 254

    11. 7.11 Views: Shallow Copies 256

    12. 7.12 Deep Copies 258

    13. 7.13 Reshaping and Transposing 259

    14. 7.14 Intro to Data Science: pandas Series and DataFrames 262

      1. 7.14.1 pandas Series 262

      2. 7.14.2 DataFrames 267

    15. 7.15 Wrap-Up 275

  8. 8 Strings: A Deeper Look 283

    1. 8.1 Introduction 284

    2. 8.2 Formatting Strings 285

      1. 8.2.1 Presentation Types 285

      2. 8.2.2 Field Widths and Alignment 286

      3. 8.2.3 Numeric Formatting 287

      4. 8.2.4 String’s format Method 288

    3. 8.3 Concatenating and Repeating Strings 289

    4. 8.4 Stripping Whitespace from Strings 290

    5. 8.5 Changing Character Case 291

    6. 8.6 Comparison Operators for Strings 292

    7. 8.7 Searching for Substrings 292

    8. 8.8 Replacing Substrings 294

    9. 8.9 Splitting and Joining Strings 294

    10. 8.10 Characters and Character-Testing Methods 297

    11. 8.11 Raw Strings 298

    12. 8.12 Introduction to Regular Expressions 299

      1. 8.12.1 re Module and Function fullmatch 300

      2. 8.12.2 Replacing Substrings and Splitting Strings 303

      3. 8.12.3 Other Search Functions; Accessing Matches 304

    13. 8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 307

    14. 8.14 Wrap-Up 312

  9. 9 Files and Exceptions 319

    1. 9.1 Introduction 320

    2. 9.2 Files 321

    3. 9.3 Text-File Processing 321

      1. 9.3.1 Writing to a Text File: Introducing the with Statement 322

      2. 9.3.2 Reading Data from a Text File 323

    4. 9.4 Updating Text Files 325

    5. 9.5 Serialization with JSON 327

    6. 9.6 Focus on Security: pickle Serialization and Deserialization 330

    7. 9.7 Additional Notes Regarding Files 330

    8. 9.8 Handling Exceptions 331

      1. 9.8.1 Division by Zero and Invalid Input 332

      2. 9.8.2 try Statements 332

      3. 9.8.3 Catching Multiple Exceptions in One except Clause 335

      4. 9.8.4 What Exceptions Does a Function or Method Raise? 336

      5. 9.8.5 What Code Should Be Placed in a try Suite? 336

    9. 9.9 finally Clause 336

    10. 9.10 Explicitly Raising an Exception 339

    11. 9.11 (Optional) Stack Unwinding and Tracebacks 339

    12. 9.12 Intro to Data Science: Working with CSV Files 342

      1. 9.12.1 Python Standard Library Module csv 342

      2. 9.12.2 Reading CSV Files into Pandas DataFrames 344

      3. 9.12.3 Reading the Titanic Disaster Dataset 346

      4. 9.12.4 Simple Data Analysis with the Titanic Disaster Dataset 347

      5. 9.12.5 Passenger Age Histogram 348

    13. 9.13 Wrap-Up 349

  10. 10 Object-Oriented Programming 355

    1. 10.1 Introduction 356

    2. 10.2 Custom Class Account 358

      1. 10.2.1 Test-Driving Class Account 358

      2. 10.2.2 Account Class Definition 360

      3. 10.2.3 Composition: Object References as Members of Classes 361

    3. 10.3 Controlling Access to Attributes 363

    4. 10.4 Properties for Data Access 364

      1. 10.4.1 Test-Driving Class Time 364

      2. 10.4.2 Class Time Definition 366

      3. 10.4.3 Class Time Definition Design Notes 370

    5. 10.5 Simulating “Private” Attributes 371

    6. 10.6 Case Study: Card Shuffling and Dealing Simulation 373

      1. 10.6.1 Test-Driving Classes Card and DeckOfCards 373

      2. 10.6.2 Class Card—Introducing Class Attributes 375

      3. 10.6.3 Class DeckOfCards 377

      4. 10.6.4 Displaying Card Images with Matplotlib 378

    7. 10.7 Inheritance: Base Classes and Subclasses 382

    8. 10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 384

      1. 10.8.1 Base Class CommissionEmployee 384

      2. 10.8.2 Subclass SalariedCommissionEmployee 387

      3. 10.8.3 Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically 391

      4. 10.8.4 A Note About Object-Based and Object-Oriented Programming 391

    9. 10.9 Duck Typing and Polymorphism 392

    10. 10.10 Operator Overloading 393

      1. 10.10.1 Test-Driving Class Complex 394

      2. 10.10.2 Class Complex Definition 395

    11. 10.11 Exception Class Hierarchy and Custom Exceptions 397

    12. 10.12 Named Tuples 399

    13. 10.13 A Brief Intro to Python 3.7’s New Data Classes 400

      1. 10.13.1 Creating a Card Data Class 401

      2. 10.13.2 Using the Card Data Class 403

      3. 10.13.3 Data Class Advantages over Named Tuples 405

      4. 10.13.4 Data Class Advantages over Traditional Classes 406

    14. 10.14 Unit Testing with Docstrings and doctest 406

    15. 10.15 Namespaces and Scopes 411

    16. 10.16 Intro to Data Science: Time Series and Simple Linear Regression 414

    17. 10.17 Wrap-Up 423

  11. 11 Computer Science Thinking: Recursion, Searching, Sorting and Big O 431

    1. 11.1 Introduction 432

    2. 11.2 Factorials 433

    3. 11.3 Recursive Factorial Example 433

    4. 11.4 Recursive Fibonacci Series Example 436

    5. 11.5 Recursion vs. Iteration 439

    6. 11.6 Searching and Sorting 440

    7. 11.7 Linear Search 440

    8. 11.8 Efficiency of Algorithms: Big O 442

    9. 11.9 Binary Search 444

      1. 11.9.1 Binary Search Implementation 445

      2. 11.9.2 Big O of the Binary Search 447

    10. 11.10 Sorting Algorithms 448

    11. 11.11 Selection Sort 448

      1. 11.11.1 Selection Sort Implementation 449

      2. 11.11.2 Utility Function print_pass 450

      3. 11.11.3 Big O of the Selection Sort 451

    12. 11.12 Insertion Sort 451

      1. 11.12.1 Insertion Sort Implementation 452

      2. 11.12.2 Big O of the Insertion Sort 453

    13. 11.13 Merge Sort 454

      1. 11.13.1 Merge Sort Implementation 454

      2. 11.13.2 Big O of the Merge Sort 459

    14. 11.14 Big O Summary for This Chapter’s Searching and Sorting Algorithms 459

    15. 11.15 Visualizing Algorithms 460

      1. 11.15.1 Generator Functions 462

      2. 11.15.2 Implementing the Selection Sort Animation 463

    16. 11.16 Wrap-Up 468

  12. 12 Natural Language Processing (NLP) 477

    1. 12.1 Introduction 478

    2. 12.2 TextBlob 479

      1. 12.2.1 Create a TextBlob 481

      2. 12.2.2 Tokenizing Text into Sentences and Words 482

      3. 12.2.3 Parts-of-Speech Tagging 482

      4. 12.2.4 Extracting Noun Phrases 483

      5. 12.2.5 Sentiment Analysis with TextBlob’s Default Sentiment Analyzer 484

      6. 12.2.6 Sentiment Analysis with the NaiveBayesAnalyzer 486

      7. 12.2.7 Language Detection and Translation 487

      8. 12.2.8 Inflection: Pluralization and Singularization 489

      9. 12.2.9 Spell Checking and Correction 489

      10. 12.2.10 Normalization: Stemming and Lemmatization 490

      11. 12.2.11 Word Frequencies 491

      12. 12.2.12 Getting Definitions, Synonyms and Antonyms from WordNet 492

      13. 12.2.13 Deleting Stop Words 494

      14. 12.2.14 n-grams 496

    3. 12.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 497

      1. 12.3.1 Visualizing Word Frequencies with Pandas 497

      2. 12.3.2 Visualizing Word Frequencies with Word Clouds 500

    4. 12.4 Readability Assessment with Textatistic 503

    5. 12.5 Named Entity Recognition with spaCy 505

    6. 12.6 Similarity Detection with spaCy 507

    7. 12.7 Other NLP Libraries and Tools 509

    8. 12.8 Machine Learning and Deep Learning Natural Language Applications 509

    9. 12.9 Natural Language Datasets 510

    10. 12.10 Wrap-Up 510

  13. 13 Data Mining Twitter 515

    1. 13.1 Introduction 516

    2. 13.2 Overview of the Twitter APIs 518

    3. 13.3 Creating a Twitter Account 519

    4. 13.4 Getting Twitter Credentials—Creating an App 520

    5. 13.5 What’s in a Tweet? 521

    6. 13.6 Tweepy 525

    7. 13.7 Authenticating with Twitter Via Tweepy 525

    8. 13.8 Getting Information About a Twitter Account 527

    9. 13.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 529

      1. 13.9.1 Determining an Account’s Followers 529

      2. 13.9.2 Determining Whom an Account Follows 532

      3. 13.9.3 Getting a User’s Recent Tweets 532

    10. 13.10 Searching Recent Tweets 534

    11. 13.11 Spotting Trends: Twitter Trends API 536

      1. 13.11.1 Places with Trending Topics 536

      2. 13.11.2 Getting a List of Trending Topics 537

      3. 13.11.3 Create a Word Cloud from Trending Topics 539

    12. 13.12 Cleaning/Preprocessing Tweets for Analysis 541

    13. 13.13 Twitter Streaming API 542

      1. 13.13.1 Creating a Subclass of StreamListener 543

      2. 13.13.2 Initiating Stream Processing 545

    14. 13.14 Tweet Sentiment Analysis 547

    15. 13.15 Geocoding and Mapping 551

      1. 13.15.1 Getting and Mapping the Tweets 552

      2. 13.15.2 Utility Functions in tweetutilities.py 556

      3. 13.15.3 Class LocationListener 558

    16. 13.16 Ways to Store Tweets 559

    17. 13.17 Twitter and Time Series 560

    18. 13.18 Wrap-Up 560

  14. 14 IBM Watson and Cognitive Computing 565

    1. 14.1 Introduction: IBM Watson and Cognitive Computing 566

    2. 14.2 IBM Cloud Account and Cloud Console 568

    3. 14.3 Watson Services 568

    4. 14.4 Additional Services and Tools 572

    5. 14.5 Watson Developer Cloud Python SDK 573

    6. 14.6 Case Study: Traveler’s Companion Translation App 574

      1. 14.6.1 Before You Run the App 575

      2. 14.6.2 Test-Driving the App 576

      3. 14.6.3 SimpleLanguageTranslator.py Script Walkthrough 577

    7. 14.7 Watson Resources 587

    8. 14.8 Wrap-Up 589

  15. 15 Machine Learning: Classification, Regression and Clustering 593

    1. 15.1 Introduction to Machine Learning 594

      1. 15.1.1 Scikit-Learn 595

      2. 15.1.2 Types of Machine Learning 596

      3. 15.1.3 Datasets Bundled with Scikit-Learn 598

      4. 15.1.4 Steps in a Typical Data Science Study 599

    2. 15.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 599

      1. 15.2.1 k-Nearest Neighbors Algorithm 601

      2. 15.2.2 Loading the Dataset 602

      3. 15.2.3 Visualizing the Data 606

      4. 15.2.4 Splitting the Data for Training and Testing 608

      5. 15.2.5 Creating the Model 609

      6. 15.2.6 Training the Model 610

      7. 15.2.7 Predicting Digit Classes 610

    3. 15.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 612

      1. 15.3.1 Metrics for Model Accuracy 612

      2. 15.3.2 K-Fold Cross-Validation 616

      3. 15.3.3 Running Multiple Models to Find the Best One 617

      4. 15.3.4 Hyperparameter Tuning 619

    4. 15.4 Case Study: Time Series and Simple Linear Regression 620

    5. 15.5 Case Study: Multiple Linear Regression with the California Housing Dataset 625

      1. 15.5.1 Loading the Dataset 626

      2. 15.5.2 Exploring the Data with Pandas 628

      3. 15.5.3 Visualizing the Features 630

      4. 15.5.4 Splitting the Data for Training and Testing 634

      5. 15.5.5 Training the Model 634

      6. 15.5.6 Testing the Model 635

      7. 15.5.7 Visualizing the Expected vs. Predicted Prices 636

      8. 15.5.8 Regression Model Metrics 637

      9. 15.5.9 Choosing the Best Model 638

    6. 15.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 639

    7. 15.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 642

      1. 15.7.1 Loading the Iris Dataset 644

      2. 15.7.2 Exploring the Iris Dataset: Descriptive Statistics with Pandas 646

      3. 15.7.3 Visualizing the Dataset with a Seaborn pairplot 647

      4. 15.7.4 Using a KMeans Estimator 650

      5. 15.7.5 Dimensionality Reduction with Principal Component Analysis 652

      6. 15.7.6 Choosing the Best Clustering Estimator 655

    8. 15.8 Wrap-Up 656

  16. 16 Deep Learning 665

    1. 16.1 Introduction 666

      1. 16.1.1 Deep Learning Applications 668

      2. 16.1.2 Deep Learning Demos 669

      3. 16.1.3 Keras Resources 669

    2. 16.2 Keras Built-In Datasets 669

    3. 16.3 Custom Anaconda Environments 670

    4. 16.4 Neural Networks 672

    5. 16.5 Tensors 674

    6. 16.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 676

      1. 16.6.1 Loading the MNIST Dataset 677

      2. 16.6.2 Data Exploration 678

      3. 16.6.3 Data Preparation 680

      4. 16.6.4 Creating the Neural Network 682

      5. 16.6.5 Training and Evaluating the Model 691

      6. 16.6.6 Saving and Loading a Model 696

    7. 16.7 Visualizing Neural Network Training with TensorBoard 697

    8. 16.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 700

    9. 16.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 701

      1. 16.9.1 Loading the IMDb Movie Reviews Dataset 702

      2. 16.9.2 Data Exploration 703

      3. 16.9.3 Data Preparation 705

      4. 16.9.4 Creating the Neural Network 706

      5. 16.9.5 Training and Evaluating the Model 709

    10. 16.10 Tuning Deep Learning Models 710

    11. 16.11 Convnet Models Pretrained on ImageNet 711

    12. 16.12 Reinforcement Learning 712

      1. 16.12.1 Deep Q-Learning 713

      2. 16.12.2 OpenAI Gym 713

    13. 16.13 Wrap-Up 714

  17. 17 Big Data: Hadoop, Spark, NoSQL and IoT 723

    1. 17.1 Introduction 724

    2. 17.2 Relational Databases and Structured Query Language (SQL) 728

      1. 17.2.1 A books Database 730

      2. 17.2.2 SELECT Queries 734

      3. 17.2.3 WHERE Clause 734

      4. 17.2.4 ORDER BY Clause 736

      5. 17.2.5 Merging Data from Multiple Tables: INNER JOIN 737

      6. 17.2.6 INSERT INTO Statement 738

      7. 17.2.7 UPDATE Statement 739

      8. 17.2.8 DELETE FROM Statement 739

    3. 17.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 741

      1. 17.3.1 NoSQL Key–Value Databases 741

      2. 17.3.2 NoSQL Document Databases 742

      3. 17.3.3 NoSQL Columnar Databases 742

      4. 17.3.4 NoSQL Graph Databases 743

      5. 17.3.5 NewSQL Databases 743

    4. 17.4 Case Study: A MongoDB JSON Document Database 744

      1. 17.4.1 Creating the MongoDB Atlas Cluster 745

      2. 17.4.2 Streaming Tweets into MongoDB 746

    5. 17.5 Hadoop 755

      1. 17.5.1 Hadoop Overview 755

      2. 17.5.2 Summarizing Word Lengths in Romeo and Juliet via MapReduce 758

      3. 17.5.3 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight 758

      4. 17.5.4 Hadoop Streaming 760

      5. 17.5.5 Implementing the Mapper 760

      6. 17.5.6 Implementing the Reducer 761

      7. 17.5.7 Preparing to Run the MapReduce Example 762

      8. 17.5.8 Running the MapReduce Job 763

    6. 17.6 Spark 766

      1. 17.6.1 Spark Overview 766

      2. 17.6.2 Docker and the Jupyter Docker Stacks 767

      3. 17.6.3 Word Count with Spark 770

      4. 17.6.4 Spark Word Count on Microsoft Azure 773

    7. 17.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 777

      1. 17.7.1 Streaming Tweets to a Socket 777

      2. 17.7.2 Summarizing Tweet Hashtags; Introducing Spark SQL 780

    8. 17.8 Internet of Things and Dashboards 786

      1. 17.8.1 Publish and Subscribe 788

      2. 17.8.2 Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard 788

      3. 17.8.3 Simulating an Internet-Connected Thermostat in Python 790

      4. 17.8.4 Creating the Dashboard with Freeboard.io 792

      5. 17.8.5 Creating a Python PubNub Subscriber 794

    9. 17.9 Wrap-Up 798

  1. Index 805