Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud, Global Edition

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
	Symbols
	Numerics
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Y
	Z
Reviewer Comments