Home > Store

Python for Programmers

Register your product to gain access to bonus material or receive a coupon.

Python for Programmers

Best Value Purchase

Book + eBook Bundle

  • Your Price: $64.79
  • List Price: $107.98
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

More Purchase Options

Book

  • Your Price: $47.99
  • List Price: $59.99
  • Usually ships in 24 hours.

eBook (Watermarked)

  • Your Price: $38.39
  • List Price: $47.99
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

Description

  • Copyright 2019
  • Dimensions: 7" x 9-1/8"
  • Pages: 640
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-522433-0
  • ISBN-13: 978-0-13-522433-5

The professional programmer’s Deitel® guide to Python® with introductory artificial intelligence case studies

Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today’s most compelling, leading-edge computing technologies and programming in Python—one of the world’s most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details.

In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you’ll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you’ll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® Watson™, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, Spark™ and NoSQL databases, the Internet of Things and more. You’ll also work directly or indirectly with cloud-based services, including Twitter, Google Translate™, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more.

Features

  • 500+ hands-on, real-world, live-code examples from snippets to case studies
  • IPython + code in Jupyter® Notebooks
  • Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code
  • Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions
  • Procedural, functional-style and object-oriented programming
  • Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames
  • Static, dynamic and interactive visualizations
  • Data experiences with real-world datasets and data sources
  • Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression
  • AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® Watson™, machine learning, deep learning, computer vision, Hadoop®, Spark™, NoSQL, IoT
  • Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more

Register your product for convenient access to downloads, updates, and/or corrections as they become available. See inside book for more information.

Downloads

Downloads

Example Files (6.6 MB .zip)

Sample Content

Online Sample Chapter

How to Sort a List of Tuples in Python

Sample Pages

Download the sample pages (includes Chapter 5)

Download: Color-Coded Table of Contents (543 KB .pdf)

Table of Contents

Preface xvii
Before You Begin xxxiii


Chapter 1: Introduction to Computers and Python 1
1.1 Introduction 2
1.2 A Quick Review of Object Technology Basics 3
1.3 Python 5
1.4 It’s the Libraries! 7
1.5 Test-Drives: Using IPython and Jupyter Notebooks 9
1.6 The Cloud and the Internet of Things 16
1.7 How Big Is Big Data? 17
1.8 Case Study—A Big-Data Mobile Application 24
1.9 Intro to Data Science: Artificial Intelligence—at the Intersection of CS and Data Science 26
1.10 Wrap-Up 29

Chapter 2: Introduction to Python Programming 31
2.1 Introduction 32
2.2 Variables and Assignment Statements 32
2.3 Arithmetic 33
2.4 Function print and an Intro to Single- and Double-Quoted Strings 36
2.5 Triple-Quoted Strings 38
2.6 Getting Input from the User 39
2.7 Decision Making: The if Statement and Comparison Operators 41
2.8 Objects and Dynamic Typing 45
2.9 Intro to Data Science: Basic Descriptive Statistics 46
2.10 Wrap-Up 48

Chapter 3: Control Statements 49
3.1 Introduction 50
3.2 Control Statements 50
3.3 if Statement 51
3.4 if…else and if…elif…else Statements 52
3.5 while Statement 55
3.6 for Statement 55
3.7 Augmented Assignments 57
3.8 Sequence-Controlled Iteration; Formatted Strings 58
3.9 Sentinel-Controlled Iteration 59
3.10 Built-In Function range: A Deeper Look 60
3.11 Using Type Decimal for Monetary Amounts 61
3.12 break and continue Statements 64
3.13 Boolean Operators and, or and not 65
3.14 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode 67
3.15 Wrap-Up 69

Chapter 4: Functions 71
4.1 Introduction 72
4.2 Defining Functions 72
4.3 Functions with Multiple Parameters 75
4.4 Random-Number Generation 76
4.5 Case Study: A Game of Chance 78
4.6 Python Standard Library 81
4.7 math Module Functions 82
4.8 Using IPython Tab Completion for Discovery 83
4.9 Default Parameter Values 85
4.10 Keyword Arguments 85
4.11 Arbitrary Argument Lists 86
4.12 Methods: Functions That Belong to Objects 87
4.13 Scope Rules 87
4.14 import: A Deeper Look 89
4.15 Passing Arguments to Functions: A Deeper Look 90
4.16 Recursion 93
4.17 Functional-Style Programming 95
4.18 Intro to Data Science: Measures of Dispersion 97
4.19 Wrap-Up 98

Chapter 5: Sequences: Lists and Tuples 101
5.1 Introduction 102
5.2 Lists 102
5.3 Tuples 106
5.4 Unpacking Sequences 108
5.5 Sequence Slicing 110
5.6 del Statement 112
5.7 Passing Lists to Functions 113
5.8 Sorting Lists 115
5.9 Searching Sequences 116
5.10 Other List Methods 117
5.11 Simulating Stacks with Lists 119
5.12 List Comprehensions 120
5.13 Generator Expressions 121
5.14 Filter, Map and Reduce 122
5.15 Other Sequence Processing Functions 124
5.16 Two-Dimensional Lists 126
5.17 Intro to Data Science: Simulation and Static Visualizations 128
5.18 Wrap-Up 135

Chapter 6: Dictionaries and Sets 137
6.1 Introduction 138
6.2 Dictionaries 138
6.3 Sets 147
6.4 Intro to Data Science: Dynamic Visualizations 152
6.5 Wrap-Up 158

Chapter 7: Array-Oriented Programming with NumPy 159
7.1 Introduction 160
7.2 Creating arrays from Existing Data 160
7.3 array Attributes 161
7.4 Filling arrays with Specific Values 163
7.5 Creating arrays from Ranges 164
7.6 List vs. array Performance: Introducing %timeit 165
7.7 array Operators 167
7.8 NumPy Calculation Methods 169
7.9 Universal Functions 170
7.10 Indexing and Slicing 171
7.11 Views: Shallow Copies 173
7.12 Deep Copies 174
7.13 Reshaping and Transposing 175
7.14 Intro to Data Science: pandas Series and DataFrames 177
7.15 Wrap-Up 189

Chapter 8: Strings: A Deeper Look 191
8.1 Introduction 192
8.2 Formatting Strings 193
8.3 Concatenating and Repeating Strings 196
8.4 Stripping Whitespace from Strings 197
8.5 Changing Character Case 197
8.6 Comparison Operators for Strings 198
8.7 Searching for Substrings 198
8.8 Replacing Substrings 199
8.9 Splitting and Joining Strings 200
8.10 Characters and Character-Testing Methods 202
8.11 Raw Strings 203
8.12 Introduction to Regular Expressions 203
8.13 Intro to Data Science: Pandas, Regular Expressions and Data Munging 210
8.14 Wrap-Up 214

Chapter 9: Files and Exceptions 217
9.1 Introduction 218
9.2 Files 219
9.3 Text-File Processing 219
9.4 Updating Text Files 222
9.5 Serialization with JSON 223
9.6 Focus on Security: pickle Serialization and Deserialization 226
9.7 Additional Notes Regarding Files 226
9.8 Handling Exceptions 227
9.9 finally Clause 231
9.10 Explicitly Raising an Exception 233
9.11 (Optional) Stack Unwinding and Tracebacks 233
9.12 Intro to Data Science: Working with CSV Files 235
9.13 Wrap-Up 241

Chapter 10: Object-Oriented Programming 243
10.1 Introduction 244
10.2 Custom Class Account 246
10.3 Controlling Access to Attributes 249
10.4 Properties for Data Access 250
10.5 Simulating “Private” Attributes 256
10.6 Case Study: Card Shuffling and Dealing Simulation 258
10.7 Inheritance: Base Classes and Subclasses 266
10.8 Building an Inheritance Hierarchy; Introducing Polymorphism 267
10.9 Duck Typing and Polymorphism 275
10.10 Operator Overloading 276
10.11 Exception Class Hierarchy and Custom Exceptions 279
10.12 Named Tuples 280
10.13 A Brief Intro to Python 3.7’s New Data Classes 281
10.14 Unit Testing with Docstrings and doctest 287
10.15 Namespaces and Scopes 290
10.16 Intro to Data Science: Time Series and Simple Linear Regression 293
10.17 Wrap-Up 301

Chapter 11: Natural Language Processing (NLP) 303
11.1 Introduction 304
11.2 TextBlob 305
11.3 Visualizing Word Frequencies with Bar Charts and Word Clouds 319
11.4 Readability Assessment with Textatistic 324
11.5 Named Entity Recognition with spaCy 326
11.6 Similarity Detection with spaCy 327
11.7 Other NLP Libraries and Tools 328
11.8 Machine Learning and Deep Learning Natural Language Applications 328
11.9 Natural Language Datasets 329
11.10 Wrap-Up 330

Chapter 12: Data Mining Twitter 331
12.1 Introduction 332
12.2 Overview of the Twitter APIs 334
12.3 Creating a Twitter Account 335
12.4 Getting Twitter Credentials—Creating an App 335
12.5 What’s in a Tweet? 337
12.6 Tweepy 340
12.7 Authenticating with Twitter Via Tweepy 341
12.8 Getting Information About a Twitter Account 342
12.9 Introduction to Tweepy Cursors: Getting an Account’s Followers and Friends 344
12.10 Searching Recent Tweets 347
12.11 Spotting Trends: Twitter Trends API 349
12.12 Cleaning/Preprocessing Tweets for Analysis 353
12.13 Twitter Streaming API 354
12.14 Tweet Sentiment Analysis 359
12.15 Geocoding and Mapping 362
12.16 Ways to Store Tweets 370
12.17 Twitter and Time Series 370
12.18 Wrap-Up 371

Chapter 13: IBM Watson and Cognitive Computing 373
13.1 Introduction: IBM Watson and Cognitive Computing 374
13.2 IBM Cloud Account and Cloud Console 375
13.3 Watson Services 376
13.4 Additional Services and Tools 379
13.5 Watson Developer Cloud Python SDK 381
13.6 Case Study: Traveler’s Companion Translation App 381
13.7 Watson Resources 394
13.8 Wrap-Up 395

Chapter 14: Machine Learning: Classification, Regression and Clustering 397
14.1 Introduction to Machine Learning 398
14.2 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 1 403
14.3 Case Study: Classification with k-Nearest Neighbors and the Digits Dataset, Part 2 413
14.4 Case Study: Time Series and Simple Linear Regression 420
14.5 Case Study: Multiple Linear Regression with the California Housing Dataset 425
14.6 Case Study: Unsupervised Machine Learning, Part 1—Dimensionality Reduction 438
14.7 Case Study: Unsupervised Machine Learning, Part 2—k-Means Clustering 442
14.8 Wrap-Up 455

Chapter 15: Deep Learning 457
15.1 Introduction 458
15.2 Keras Built-In Datasets 461
15.3 Custom Anaconda Environments 462
15.4 Neural Networks 463
15.5 Tensors 465
15.6 Convolutional Neural Networks for Vision; Multi-Classification with the MNIST Dataset 467
15.7 Visualizing Neural Network Training with TensorBoard 486
15.8 ConvnetJS: Browser-Based Deep-Learning Training and Visualization 489
15.9 Recurrent Neural Networks for Sequences; Sentiment Analysis with the IMDb Dataset 489
15.10 Tuning Deep Learning Models 497
15.11 Convnet Models Pretrained on ImageNet 498
15.12 Wrap-Up 499

Chapter 16: Big Data: Hadoop, Spark, NoSQL and IoT 501
16.1 Introduction 502
16.2 Relational Databases and Structured Query Language (SQL) 506
16.3 NoSQL and NewSQL Big-Data Databases: A Brief Tour 517
16.4 Case Study: A MongoDB JSON Document Database 520
16.5 Hadoop 530
16.6 Spark 541
16.7 Spark Streaming: Counting Twitter Hashtags Using the pyspark-notebook Docker Stack 551
16.8 Internet of Things and Dashboards 560
16.9 Wrap-Up 571

Index 573

Updates

Errata

Errata (7 May 2019) (19 KB .docx)

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020