Cover
Digital Resources for Students
Intro to Python® for Computer Science and Data Science
Half Title
Copyright
Title Page
Dedication
Contents
Preface
Before You Begin
1. Introduction to Computers and Python
1.1 Introduction
1.2 Hardware and Software
1.2.1 Moore’s Law
1.2.2 Computer Organization
1.3 Data Hierarchy
1.4 Machine Languages, Assembly Languages and High-Level Languages
1.5 Introduction to Object Technology
1.6 Operating Systems
1.7 Python
1.8 It’s the Libraries!
1.8.1 Python Standard Library
1.8.2 Data-Science Libraries
1.9 Other Popular Programming Languages
1.10 Test-Drives: Using IPython and Jupyter Notebooks
1.10.1 Using IPython Interactive Mode as a Calculator
1.10.2 Executing a Python Program Using the IPython Interpreter
1.10.3 Writing and Executing Code in a Jupyter Notebook
1.11 Internet and World Wide Web
1.11.1 Internet: A Network of Networks
1.11.2 World Wide Web: Making the Internet User-Friendly
1.11.3 The Cloud
1.11.4 Internet of Things
1.12 Software Technologies
1.13 How Big Is Big Data?
1.13.1 Big Data Analytics
1.13.2 Data Science and Big Data Are Making a Difference: Use Cases
1.14 Case Study—A Big-Data Mobile Application
1.15 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science
2. Introduction to Python Programming
2.1 Introduction
2.2 Variables and Assignment Statements
2.3 Arithmetic
2.4 Function print and an Intro to Single- and Double-Quoted Strings
2.5 Triple-Quoted Strings
2.6 Getting Input from the User
2.7 Decision Making: The if Statement and Comparison Operators
2.8 Objects and Dynamic Typing
2.9 Intro to Data Science: Basic Descriptive Statistics
2.10 Wrap-Up
3. Control Statements and Program Development
3.1 Introduction
3.2 Algorithms
3.3 Pseudocode
3.4 Control Statements
3.5 if Statement
3.6 if…else and if…elif…else Statements
3.7 while Statement
3.8 for Statement
3.8.1 Iterables, Lists and Iterators
3.8.2 Built-In range Function
3.9 Augmented Assignments
3.10 Program Development: Sequence-Controlled Repetition
3.10.1 Requirements Statement
3.10.2 Pseudocode for the Algorithm
3.10.3 Coding the Algorithm in Python
3.10.4 Introduction to Formatted Strings
3.11 Program Development: Sentinel-Controlled Repetition
3.12 Program Development: Nested Control Statements
3.13 Built-In Function range: A Deeper Look
3.14 Using Type Decimal for Monetary Amounts
3.15 break and continue Statements
3.16 Boolean Operators and, or and not
3.17 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode
3.18 Wrap-Up
4. Functions
4.1 Introduction
4.2 Defining Functions
4.3 Functions with Multiple Parameters
4.4 Random-Number Generation
4.5 Case Study: A Game of Chance
4.6 Python Standard Library
4.7 math Module Functions
4.8 Using IPython Tab Completion for Discovery
4.9 Default Parameter Values
4.10 Keyword Arguments
4.11 Arbitrary Argument Lists
4.12 Methods: Functions That Belong to Objects
4.13 Scope Rules
4.14 import: A Deeper Look
4.15 Passing Arguments to Functions: A Deeper Look
4.16 Function-Call Stack
4.17 Functional-Style Programming
4.18 Intro to Data Science: Measures of Dispersion
4.19 Wrap-Up
5. Sequences: Lists and Tuples
5.1 Introduction
5.2 Lists
5.3 Tuples
5.4 Unpacking Sequences
5.5 Sequence Slicing
5.6 del Statement
5.7 Passing Lists to Functions
5.8 Sorting Lists
5.9 Searching Sequences
5.10 Other List Methods
5.11 Simulating Stacks with Lists
5.12 List Comprehensions
5.13 Generator Expressions
5.14 Filter, Map and Reduce
5.15 Other Sequence Processing Functions
5.16 Two-Dimensional Lists
5.17 Intro to Data Science: Simulation and Static Visualizations
5.17.1 Sample Graphs for 600, 60,000 and 6,000,000 Die Rolls
5.17.2 Visualizing Die-Roll Frequencies and Percentages
5.18 Wrap-Up
6. Dictionaries and Sets
6.1 Introduction
6.2 Dictionaries
6.2.1 Creating a Dictionary
6.2.2 Iterating through a Dictionary
6.2.3 Basic Dictionary Operations
6.2.4 Dictionary Methods keys and values
6.2.5 Dictionary Comparisons
6.2.6 Example: Dictionary of Student Grades
6.2.7 Example: Word Counts
6.2.8 Dictionary Method update
6.2.9 Dictionary Comprehensions
6.3 Sets
6.3.1 Comparing Sets
6.3.2 Mathematical Set Operations
6.3.3 Mutable Set Operators and Methods
6.3.4 Set Comprehensions
6.4 Intro to Data Science: Dynamic Visualizations
6.4.1 How Dynamic Visualization Works
6.4.2 Implementing a Dynamic Visualization
6.5 Wrap-Up
7. Array-Oriented Programming with NumPy
7.1 Introduction
7.2 Creating arrays from Existing Data
7.3 array Attributes
7.4 Filling arrays with Specific Values
7.5 Creating arrays from Ranges
7.6 List vs. array Performance: Introducing %timeit
7.7 array Operators
7.8 NumPy Calculation Methods
7.9 Universal Functions
7.10 Indexing and Slicing
7.11 Views: Shallow Copies
7.12 Deep Copies
7.13 Reshaping and Transposing
7.14 Intro to Data Science: pandas Series and DataFrames
7.14.1 pandas Series
7.14.2 DataFrames
7.15 Wrap-Up
8. Strings: A Deeper Look
8.1 Introduction
8.2 Formatting Strings
8.2.1 Presentation Types
8.2.2 Field Widths and Alignment
8.2.3 Numeric Formatting
8.2.4 String’s format Method
8.3 Concatenating and Repeating Strings
8.4 Stripping Whitespace from Strings
8.5 Changing Character Case
8.6 Comparison Operators for Strings
8.7 Searching for Substrings
8.8 Replacing Substrings
8.9 Splitting and Joining Strings
8.10 Characters and Character-Testing Methods
8.11 Raw Strings
8.12 Introduction to Regular Expressions
8.12.1 re Module and Function fullmatch
8.12.2 Replacing Substrings and Splitting Strings
8.12.3 Other Search Functions; Accessing Matches
8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging
8.14 Wrap-Up
9. Files and Exceptions
9.1 Introduction
9.2 Files
9.3 Text-File Processing
9.3.1 Writing to a Text File: Introducing the with Statement
9.3.2 Reading Data from a Text File
9.4 Updating Text Files
9.5 Serialization with JSON
9.6 Focus on Security: pickle Serialization and Deserialization
9.7 Additional Notes Regarding Files
9.8 Handling Exceptions
9.8.1 Division by Zero and Invalid Input
9.8.2 try Statements
9.8.3 Catching Multiple Exceptions in One except Clause
9.8.4 What Exceptions Does a Function or Method Raise?
9.8.5 What Code Should Be Placed in a try Suite?
9.9 finally Clause
9.10 Explicitly Raising an Exception
9.11 (Optional) Stack Unwinding and Tracebacks
9.12 Intro to Data Science: Working with CSV Files
9.12.1 Python Standard Library Module csv
9.12.2 Reading CSV Files into Pandas DataFrames
9.12.3 Reading the Titanic Disaster Dataset
9.12.4 Simple Data Analysis with the Titanic Disaster Dataset
9.12.5 Passenger Age Histogram
9.13 Wrap-Up
10. Object-Oriented Programming
10.1 Introduction
10.2 Custom Class Account
10.2.1 Test-Driving Class Account
10.2.2 Account Class Definition
10.2.3 Composition: Object References as Members of Classes
10.3 Controlling Access to Attributes
10.4 Properties for Data Access
10.4.1 Test-Driving Class Time
10.4.2 Class Time Definition
10.4.3 Class Time Definition Design Notes
10.5 Simulating “Private” Attributes
10.6 Case Study: Card Shuffling and Dealing Simulation
10.6.1 Test-Driving Classes Card and DeckOfCards
10.6.2 Class Card—Introducing Class Attributes
10.6.3 Class DeckOfCards
10.6.4 Displaying Card Images with Matplotlib
10.7 Inheritance: Base Classes and Subclasses
10.8 Building an Inheritance Hierarchy; Introducing Polymorphism
10.8.1 Base Class CommissionEmployee
10.8.2 Subclass SalariedCommissionEmployee
10.8.3 Processing CommissionEmployees and SalariedCommissionEmployees Polymorphically
10.8.4 A Note About Object-Based and Object-Oriented Programming
10.9 Duck Typing and Polymorphism
10.10 Operator Overloading
10.10.1 Test-Driving Class Complex
10.10.2 Class Complex Definition
10.11 Exception Class Hierarchy and Custom Exceptions
10.12 Named Tuples
10.13 A Brief Intro to Python 3.7’s New Data Classes
10.13.1 Creating a Card Data Class
10.13.2 Using the Card Data Class
10.13.3 Data Class Advantages over Named Tuples
10.13.4 Data Class Advantages over Traditional Classes
10.14 Unit Testing with Docstrings and doctest
10.15 Namespaces and Scopes
10.16 Intro to Data Science: Time Series and Simple Linear Regression
10.17 Wrap-Up
11. Computer Science Thinking: Recursion, Searching, Sorting and Big O
11.1 Introduction
11.2 Factorials
11.3 Recursive Factorial Example
11.4 Recursive Fibonacci Series Example
11.5 Recursion vs. Iteration
11.6 Searching and Sorting
11.7 Linear Search
11.8 Efficiency of Algorithms: Big O
11.9 Binary Search
11.9.1 Binary Search Implementation
11.9.2 Big O of the Binary Search
11.10 Sorting Algorithms
11.11 Selection Sort
11.11.1 Selection Sort Implementation
11.11.2 Utility Function print_pass
11.11.3 Big O of the Selection Sort
11.12 Insertion Sort
11.12.1 Insertion Sort Implementation
11.12.2 Big O of the Insertion Sort
11.13 Merge Sort
11.13.1 Merge Sort Implementation
11.13.2 Big O of the Merge Sort
11.14 Big O Summary for This Chapter’s Searching and Sorting Algorithms
11.15 Visualizing Algorithms
11.15.1 Generator Functions
11.15.2 Implementing the Selection Sort Animation
11.16 Wrap-Up
12. Natural Language Processing (NLP)
12.1 Introduction
12.2 TextBlob
12.2.1 Create a TextBlob
12.2.2 Tokenizing Text into Sentences and Words
12.2.3 Parts-of-Speech Tagging
12.2.4 Extracting Noun Phrases
12.2.5 Sentiment Analysis with TextBlob’s Default Sentiment Analyzer
12.2.6 Sentiment Analysis with the NaiveBayesAnalyzer
12.2.7 Language Detection and Translation
12.2.8 Inflection: Pluralization and Singularization
12.2.9 Spell Checking and Correction
12.2.10 Normalization: Stemming and Lemmatization
12.2.11 Word Frequencies
12.2.12 Getting Definitions, Synonyms and Antonyms from WordNet
12.2.13 Deleting Stop Words
12.2.14 n-grams
12.3 Visualizing Word Frequencies with Bar Charts and Word Clouds
12.3.1 Visualizing Word Frequencies with Pandas
12.3.2 Visualizing Word Frequencies with Word Clouds
12.4 Readability Assessment with Textatistic
12.5 Named Entity Recognition with spaCy
12.6 Similarity Detection with spaCy
12.7 Other NLP Libraries and Tools
12.8 Machine Learning and Deep Learning Natural Language Applications
12.9 Natural Language Datasets
12.10 Wrap-Up
13. Data Mining Twitter
13.1 Introduction
13.2 Overview of the Twitter APIs
13.3 Creating a Twitter Account
13.4 Getting Twitter Credentials—Creating an App
13.5 What’s in a Tweet?
13.6 Tweepy
13.7 Authenticating with Twitter Via Tweepy
13.8 Getting Information About a Twitter Account
13.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends
13.9.1 Determining an Account’s Followers
13.9.2 Determining Whom an Account Follows
13.9.3 Getting a User’s Recent Tweets
13.10 Searching Recent Tweets
13.11 Spotting Trends: Twitter Trends API
13.11.1 Places with Trending Topics
13.11.2 Getting a List of Trending Topics
13.11.3 Create a Word Cloud from Trending Topics
13.12 Cleaning/Preprocessing Tweets for Analysis
13.13 Twitter Streaming API
13.13.1 Creating a Subclass of StreamListener
13.13.2 Initiating Stream Processing
13.14 Tweet Sentiment Analysis
13.15 Geocoding and Mapping
13.15.1 Getting and Mapping the Tweets
13.15.2 Utility Functions in tweetutilities.py
13.15.3 Class LocationListener
13.16 Ways to Store Tweets
13.17 Twitter and Time Series
13.18 Wrap-Up
14. IBM Watson and Cognitive Computing
14.1 Introduction: IBM Watson and Cognitive Computing
14.2 IBM Cloud Account and Cloud Console
14.3 Watson Services
14.4 Additional Services and Tools
14.5 Watson Developer Cloud Python SDK
14.6 Case Study: Traveler’s Companion Translation App
14.6.1 Before You Run the App
14.6.2 Test-Driving the App
14.6.3 SimpleLanguageTranslator.py Script Walkthrough
14.7 Watson Resources
14.8 Wrap-Up
15. Machine Learning: Classification, Regression and Clustering
15.1 Introduction to Machine Learning
15.1.1 Scikit-Learn
15.1.2 Types of Machine Learning
15.1.3 Datasets Bundled with Scikit-Learn
15.1.4 Steps in a Typical Data Science Study
15.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1
15.2.1 k-Nearest Neighbors Algorithm
15.2.2 Loading the Dataset
15.2.3 Visualizing the Data
15.2.4 Splitting the Data for Training and Testing
15.2.5 Creating the Model
15.2.6 Training the Model
15.2.7 Predicting Digit Classes
15.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2
15.3.1 Metrics for Model Accuracy
15.3.2 K-Fold Cross-Validation
15.3.3 Running Multiple Models to Find the Best One
15.3.4 Hyperparameter Tuning
15.4 Case Study: Time Series and Simple Linear Regression
15.5 Case Study: Multiple Linear Regression with the California Housing Dataset
15.5.1 Loading the Dataset
15.5.2 Exploring the Data with Pandas
15.5.3 Visualizing the Features
15.5.4 Splitting the Data for Training and Testing
15.5.5 Training the Model
15.5.6 Testing the Model
15.5.7 Visualizing the Expected vs. Predicted Prices
15.5.8 Regression Model Metrics
15.5.9 Choosing the Best Model
15.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction
15.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering
15.7.1 Loading the Iris Dataset
15.7.2 Exploring the Iris Dataset: Descriptive Statistics with Pandas
15.7.3 Visualizing the Dataset with a Seaborn pairplot
15.7.4 Using a KMeans Estimator
15.7.5 Dimensionality Reduction with Principal Component Analysis
15.7.6 Choosing the Best Clustering Estimator
15.8 Wrap-Up
16. Deep Learning
16.1 Introduction
16.1.1 Deep Learning Applications
16.1.2 Deep Learning Demos
16.1.3 Keras Resources
16.2 Keras Built-In Datasets
16.3 Custom Anaconda Environments
16.4 Neural Networks
16.5 Tensors
16.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset
16.6.1 Loading the MNIST Dataset
16.6.2 Data Exploration
16.6.3 Data Preparation
16.6.4 Creating the Neural Network
16.6.5 Training and Evaluating the Model
16.6.6 Saving and Loading a Model
16.7 Visualizing Neural Network Training with TensorBoard
16.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization
16.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset
16.9.1 Loading the IMDb Movie Reviews Dataset
16.9.2 Data Exploration
16.9.3 Data Preparation
16.9.4 Creating the Neural Network
16.9.5 Training and Evaluating the Model
16.10 Tuning Deep Learning Models
16.11 Convnet Models Pretrained on ImageNet
16.12 Reinforcement Learning
16.12.1 Deep Q-Learning
16.12.2 OpenAI Gym
16.13 Wrap-Up
17. Big Data: Hadoop, Spark, NoSQL and IoT
17.1 Introduction
17.2 Relational Databases and Structured Query Language (SQL)
17.2.1 A books Database
17.2.2 Select Queries
17.2.3 Where Clause
17.2.4 Order by Clause
17.2.5 Merging Data from Multiple Tables: Inner Join
17.2.6 Insert into Statement
17.2.7 Update Statement
17.2.8 Delete from Statement
17.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour
17.3.1 NoSQL Key–Value Databases
17.3.2 NoSQL Document Databases
17.3.3 NoSQL Columnar Databases
17.3.4 NoSQL Graph Databases
17.3.5 NewSQL Databases
17.4 Case Study: A MongoDB JSON Document Database
17.4.1 Creating the MongoDB Atlas Cluster
17.4.2 Streaming Tweets into MongoDB
17.5 Hadoop
17.5.1 Hadoop Overview
17.5.2 Summarizing Word Lengths in Romeo and Juliet via MapReduce
17.5.3 Creating an Apache Hadoop Cluster in Microsoft Azure HDInsight
17.5.4 Hadoop Streaming
17.5.5 Implementing the Mapper
17.5.6 Implementing the Reducer
17.5.7 Preparing to Run the MapReduce Example
17.5.8 Running the MapReduce Job
17.6 Spark
17.6.1 Spark Overview
17.6.2 Docker and the Jupyter Docker Stacks
17.6.3 Word Count with Spark
17.6.4 Spark Word Count on Microsoft Azure
17.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack
17.7.1 Streaming Tweets to a Socket
17.7.2 Summarizing Tweet Hashtags; Introducing Spark SQL
17.8 Internet of Things and Dashboards
17.8.1 Publish and Subscribe
17.8.2 Visualizing a PubNub Sample Live Stream with a Freeboard Dashboard
17.8.3 Simulating an Internet-Connected Thermostat in Python
17.8.4 Creating the Dashboard with Freeboard.io
17.8.5 Creating a Python PubNub Subscriber
17.9 Wrap-Up
Index
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Numerics
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Reviewer Comments