Python for Programmers

<p>Preface xvii<br>Before You Begin xxxiii<br><strong>Chapter 1: Introduction to Computers and Python 1</strong><br>1.1 Introduction 2<br>1.2 A Quick Review of Object Technology Basics 3<br>1.3 Python 5<br>1.4 It’s the Libraries! 7<br>1.5 Test-Drives: Using IPython and Jupyter Notebooks 9<br>1.6 The Cloud and the Internet of Things 16<br>1.7 How Big Is Big Data? 17<br>1.8 Case Study—A Big-Data Mobile Application 24<br>1.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 26<br>1.10 Wrap-Up 29<br><strong>Chapter 2: Introduction to Python Programming 31</strong><br>2.1 Introduction 32<br>2.2 Variables and Assignment Statements 32<br>2.3 Arithmetic 33<br>2.4 Function print and an Intro to Single- and Double-Quoted Strings 36<br>2.5 Triple-Quoted Strings 38<br>2.6 Getting Input from the User 39<br>2.7 Decision Making: The if Statement and Comparison Operators 41<br>2.8 Objects and Dynamic Typing 45<br>2.9 Intro to Data Science: Basic Descriptive Statistics 46<br>2.10 Wrap-Up 48<br><strong>Chapter 3: Control Statements 49</strong><br>3.1 Introduction 50<br>3.2 Control Statements 50<br>3.3 if Statement 51<br>3.4 if...else and if...elif...else Statements 52<br>3.5 while Statement 55<br>3.6 for Statement 55<br>3.7 Augmented Assignments 57<br>3.8 Sequence-Controlled Iteration; Formatted Strings 58<br>3.9 Sentinel-Controlled Iteration 59<br>3.10 Built-In Function range: A Deeper Look 60<br>3.11 Using Type Decimal for Monetary Amounts 61<br>3.12 break and continue Statements 64<br>3.13 Boolean Operators and, or and not 65<br>3.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 67<br>3.15 Wrap-Up 69<br><strong>Chapter 4: Functions 71</strong><br>4.1 Introduction 72<br>4.2 Defining Functions 72<br>4.3 Functions with Multiple Parameters 75<br>4.4 Random-Number Generation 76<br>4.5 Case Study: A Game of Chance 78<br>4.6 Python Standard Library 81<br>4.7 math Module Functions 82<br>4.8 Using IPython Tab Completion for Discovery 83<br>4.9 Default Parameter Values 85<br>4.10 Keyword Arguments 85<br>4.11 Arbitrary Argument Lists 86<br>4.12 Methods: Functions That Belong to Objects 87<br>4.13 Scope Rules 87<br>4.14 import: A Deeper Look 89<br>4.15 Passing Arguments to Functions: A Deeper Look 90<br>4.16 Recursion 93<br>4.17 Functional-Style Programming 95<br>4.18 Intro to Data Science: Measures of Dispersion 97<br>4.19 Wrap-Up 98<br><strong>Chapter 5: Sequences: Lists and Tuples 101</strong><br>5.1 Introduction 102<br>5.2 Lists 102<br>5.3 Tuples 106<br>5.4 Unpacking Sequences 108<br>5.5 Sequence Slicing 110<br>5.6 del Statement 112<br>5.7 Passing Lists to Functions 113<br>5.8 Sorting Lists 115<br>5.9 Searching Sequences 116<br>5.10 Other List Methods 117<br>5.11 Simulating Stacks with Lists 119<br>5.12 List Comprehensions 120<br>5.13 Generator Expressions 121<br>5.14 Filter, Map and Reduce 122<br>5.15 Other Sequence Processing Functions 124<br>5.16 Two-Dimensional Lists 126<br>5.17 Intro to Data Science: Simulation and Static Visualizations 128<br>5.18 Wrap-Up 135<br><strong>Chapter 6: Dictionaries and Sets 137</strong><br>6.1 Introduction 138<br>6.2 Dictionaries 138<br>6.3 Sets 147<br>6.4 Intro to Data Science: Dynamic Visualizations 152<br>6.5 Wrap-Up 158<br><strong>Chapter 7: Array-Oriented Programming with NumPy 159</strong><br>7.1 Introduction 160<br>7.2 Creating arrays from Existing Data 160<br>7.3 array Attributes 161<br>7.4 Filling arrays with Specific Values 163<br>7.5 Creating arrays from Ranges 164<br>7.6 List vs. array Performance: Introducing %timeit 165<br>7.7 array Operators 167<br>7.8 NumPy Calculation Methods 169<br>7.9 Universal Functions 170<br>7.10 Indexing and Slicing 171<br>7.11 Views: Shallow Copies 173<br>7.12 Deep Copies 174<br>7.13 Reshaping and Transposing 175<br>7.14 Intro to Data Science: pandas Series and DataFrames 177<br>7.15 Wrap-Up 189<br><strong>Chapter 8: Strings: A Deeper Look 191</strong><br>8.1 Introduction 192<br>8.2 Formatting Strings 193<br>8.3 Concatenating and Repeating Strings 196<br>8.4 Stripping Whitespace from Strings 197<br>8.5 Changing Character Case 197<br>8.6 Comparison Operators for Strings 198<br>8.7 Searching for Substrings 198<br>8.8 Replacing Substrings 199<br>8.9 Splitting and Joining Strings 200<br>8.10 Characters and Character-Testing Methods 202<br>8.11 Raw Strings 203<br>8.12 Introduction to Regular Expressions 203<br>8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 210<br>8.14 Wrap-Up 214<br><strong>Chapter 9: Files and Exceptions 217</strong><br>9.1 Introduction 218<br>9.2 Files 219<br>9.3 Text-File Processing 219<br>9.4 Updating Text Files 222<br>9.5 Serialization with JSON 223<br>9.6 Focus on Security: pickle Serialization and Deserialization 226<br>9.7 Additional Notes Regarding Files 226<br>9.8 Handling Exceptions 227<br>9.9 finally Clause 231<br>9.10 Explicitly Raising an Exception 233<br>9.11 (Optional) Stack Unwinding and Tracebacks 233<br>9.12 Intro to Data Science: Working with CSV Files 235<br>9.13 Wrap-Up 241<br><strong>Chapter 10: Object-Oriented Programming 243</strong><br>10.1 Introduction 244<br>10.2 Custom Class Account 246<br>10.3 Controlling Access to Attributes 249<br>10.4 Properties for Data Access 250<br>10.5 Simulating “Private” Attributes 256<br>10.6 Case Study: Card Shuffling and Dealing Simulation 258<br>10.7 Inheritance: Base Classes and Subclasses 266<br>10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 267<br>10.9 Duck Typing and Polymorphism 275<br>10.10 Operator Overloading 276<br>10.11 Exception Class Hierarchy and Custom Exceptions 279<br>10.12 Named Tuples 280<br>10.13 A Brief Intro to Python 3.7’s New Data Classes 281<br>10.14 Unit Testing with Docstrings and doctest 287<br>10.15 Namespaces and Scopes 290<br>10.16 Intro to Data Science: Time Series and Simple Linear Regression 293<br>10.17 Wrap-Up 301<br><strong>Chapter 11: Natural Language Processing (NLP) 303</strong><br>11.1 Introduction 304<br>11.2 TextBlob 305<br>11.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 319<br>11.4 Readability Assessment with Textatistic 324<br>11.5 Named Entity Recognition with spaCy 326<br>11.6 Similarity Detection with spaCy 327<br>11.7 Other NLP Libraries and Tools 328<br>11.8 Machine Learning and Deep Learning Natural Language Applications 328<br>11.9 Natural Language Datasets 329<br>11.10 Wrap-Up 330<br><strong>Chapter 12: Data Mining Twitter 331</strong><br>12.1 Introduction 332<br>12.2 Overview of the Twitter APIs 334<br>12.3 Creating a Twitter Account 335<br>12.4 Getting Twitter Credentials—Creating an App 335<br>12.5 What’s in a Tweet? 337<br>12.6 Tweepy 340<br>12.7 Authenticating with Twitter Via Tweepy 341<br>12.8 Getting Information About a Twitter Account 342<br>12.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 344<br>12.10 Searching Recent Tweets 347<br>12.11 Spotting Trends: Twitter Trends API 349<br>12.12 Cleaning/Preprocessing Tweets for Analysis 353<br>12.13 Twitter Streaming API 354<br>12.14 Tweet Sentiment Analysis 359<br>12.15 Geocoding and Mapping 362<br>12.16 Ways to Store Tweets 370<br>12.17 Twitter and Time Series 370<br>12.18 Wrap-Up 371<br><strong>Chapter 13: IBM Watson and Cognitive Computing 373</strong><br>13.1 Introduction: IBM Watson and Cognitive Computing 374<br>13.2 IBM Cloud Account and Cloud Console 375<br>13.3 Watson Services 376<br>13.4 Additional Services and Tools 379<br>13.5 Watson Developer Cloud Python SDK 381<br>13.6 Case Study: Traveler’s Companion Translation App 381<br>13.7 Watson Resources 394<br>13.8 Wrap-Up 395<br><strong>Chapter 14: Machine Learning: Classification, Regression and Clustering 397</strong><br>14.1 Introduction to Machine Learning 398<br>14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 403<br>14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 413<br>14.4 Case Study: Time Series and Simple Linear Regression 420<br>14.5 Case Study: Multiple Linear Regression with the California Housing Dataset 425<br>14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 438<br>14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 442<br>14.8 Wrap-Up 455<br><strong>Chapter 15: Deep Learning 457</strong><br>15.1 Introduction 458<br>15.2 Keras Built-In Datasets 461<br>15.3 Custom Anaconda Environments 462<br>15.4 Neural Networks 463<br>15.5 Tensors 465<br>15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 467<br>15.7 Visualizing Neural Network Training with TensorBoard 486<br>15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 489<br>15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 489<br>15.10 Tuning Deep Learning Models 497<br>15.11 Convnet Models Pretrained on ImageNet 498<br>15.12 Wrap-Up 499<br><strong>Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 501</strong><br>16.1 Introduction 502<br>16.2 Relational Databases and Structured Query Language (SQL) 506<br>16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 517<br>16.4 Case Study: A MongoDB JSON Document Database 520<br>16.5 Hadoop 530<br>16.6 Spark 541<br>16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 551<br>16.8 Internet of Things and Dashboards 560<br>16.9 Wrap-Up 571<br>Index 573</p>