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Python 3

Python 3: The Comprehensive Guide

Rheinwerk Publishing, Inc

Johannes Ernesti

Peter Kaiser

Can$62.09 ~~Can$68.99~~

eBook Jun 2025 1039 pages 1st Edition

Can$62.09 ~~Can$68.99~~

Rheinwerk Publishing, Inc

Johannes Ernesti

Peter Kaiser

Can$62.09 ~~Can$68.99~~

eBook Jun 2025 1039 pages 1st Edition

Can$62.09 ~~Can$68.99~~

Can$62.09 ~~Can$68.99~~

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB format

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Key benefits

Offers an all-in-one resource spanning syntax, libraries, and frameworks
Designed to meet real-world demands across development and data workflows
Structured for progressive learning from foundations to deployment scenarios

Description

This in-depth guide to Python 3 begins by helping readers install the language and understand its core syntax through interactive exploration. Early chapters cover variables, control structures, functions, and data types like lists, tuples, dictionaries, and sets. Readers then move into file handling, error management, and object-oriented programming, building a solid foundation for real-world development. As the journey continues, the book introduces advanced concepts including decorators, generators, type hints, structural pattern matching, and context managers. It thoroughly explores the Python standard library, with practical applications in math, file systems, logging, regular expressions, parallel processing, and debugging. Readers also learn how to manage packages, virtual environments, and distributions. Later chapters shift to applied development—building GUIs with tkinter and PySide6, creating web applications with Django, and working with scientific tools like NumPy, pandas, and SciPy. The book concludes with insights on using alternative interpreters, localization, and migrating from Python 2 to 3. This resource grows with the reader, from basics to expert-level Python programming.

Who is this book for?

Aimed at developers, data scientists, engineers, and computer science students, this book assumes a basic understanding of programming logic but no prior Python experience. It suits both self-learners and those in formal education or technical professions.

What you will learn

Explore Python syntax, control flow, and core structures
Implement object-oriented and modular program designs
Manage files, exceptions, and system-level interactions
Navigate built-in types like lists, sets, and dictionaries
Create web, GUI, and network apps using standard libraries
Apply scientific tools like NumPy, pandas, and matplotlib

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB format

DRM FREE - Read whenever, wherever and however you want

Product Details

Publication date : Jun 12, 2025

Length: 1039 pages

Edition : 1st

Language : English

ISBN-13 : 9781806102464

Category :

Programming

Concepts :

Programming Language

Table of Contents

58 Chapters

Dear Reader

Notes on Usage

Table of Contents

Table of Contents

1 Introduction

1.1 Why Did We Write This Book?

1.2 What Does This Book Provide?

1.3 Structure of the Book

1.4 How Should You Read This Book?

1.5 Sample Programs

1.6 Preface To the First English Edition (2022)

1.7 Acknowledgments

2 The Python Programming Language

2 The Python Programming Language

2.1 History, Concepts, and Areas of Application

2.2 Installing Python

2.3 Installing Third-Party Modules

2.4 Using Python

Part I Getting Started with Python

Part I Getting Started with Python

3 Getting Started with the Interactive Mode

3 Getting Started with the Interactive Mode

3.3 Character Strings

3.5 Dictionaries

3.7 Logical Expressions

3.8 Functions and Methods

3.9 Screen Outputs

4 The Path to the First Program

4 The Path to the First Program

4.1 Typing, Compiling, and Testing

4.2 Basic Structure of a Python Program

4.3 The First Program

4.5 In Case of Error

5 Control Structures

5 Control Structures

5.1 Conditionals

5.3 The pass Statement

5.4 Assignment Expressions

6 Files

6.1 Data Streams

6.2 Reading Data from a File

6.3 Writing Data to a File

6.4 Generating the File Object

7 The Data Model

7 The Data Model

7.1 The Structure of Instances

7.2 Deleting References

7.3 Mutable versus Immutable Data Types

8 Functions, Methods, and Attributes

8 Functions, Methods, and Attributes

8.1 Parameters of Functions and Methods

9 Sources of Information on Python

9 Sources of Information on Python

9.1 The Built-In Help Function

9.2 The Online Documentation

Part II Data Types

Part II Data Types

10 Basic Data Types: An Overview

10 Basic Data Types: An Overview

10.1 Nothingness: NoneType

11 Numeric Data Types

11 Numeric Data Types

11.1 Arithmetic Operators

11.2 Comparison Operators

11.3 Conversion between Numeric Data Types

11.4 Integers: int

11.5 Floats: float

11.6 Boolean Values: bool

11.7 Complex Numbers: complex

12 Sequential Data Types

12 Sequential Data Types

12.1 The Difference between Text and Binary Data

12.2 Operations on Instances of Sequential Data Types

12.3 The list Data Type

12.4 Immutable Lists: tuple

12.5 Strings: str, bytes, bytearray

13 Mappings and Sets

13 Mappings and Sets

13.1 Dictionary: dict

13.2 Sets: set and frozenset

14 Collections

14.1 Chained Dictionaries

14.2 Counting Frequencies

14.3 Dictionaries with Default Values

14.4 Doubly Linked Lists

14.5 Named Tuples

15 Date and Time

15 Date and Time

15.1 Elementary Time Functions—time

15.2 Object-Oriented Date Management: datetime

15.3 Time Zones: zoneinfo

16 Enumerations and Flags

16 Enumerations and Flags

16.1 Enumeration Types: enum

16.2 Enumeration Types for Bit Patterns: flag

16.3 Integer Enumeration Types: IntEnum

Part III Advanced Programming Techniques

Part III Advanced Programming Techniques

17 Functions

17.1 Defining a Function

17.2 Return Values

17.3 Function Objects

17.4 Optional Parameters

17.5 Keyword Arguments

17.6 Arbitrarily Many Parameters

17.7 Keyword-Only Parameters

17.8 Positional-Only Parameters

17.9 Unpacking When Calling a Function

17.10 Side Effects

17.11 Namespaces

17.12 Anonymous Functions

17.13 Recursion

17.14 Built-In Functions

18 Modules and Packages

18 Modules and Packages

18.1 Importing Global Modules

18.2 Local Modules

18.4 The importlib Package

18.5 Planned Language Elements

19 Object-Oriented Programming

19 Object-Oriented Programming

19.1 Example: A Non-Object-Oriented Account

19.3 Inheritance

19.4 Multiple Inheritance

19.5 Property Attributes

19.6 Static Methods

19.7 Class Methods

19.8 Class Attributes

19.9 Built-in Functions for Object-Oriented Programming

19.10 Inheriting Built-In Data Types

19.11 Magic Methods and Magic Attributes

19.12 Data Classes

20 Exception Handling

20 Exception Handling

20.1 Exceptions

20.2 Assertions

21 Generators and Iterators

21 Generators and Iterators

21.1 Generators

21.3 Special Generators: itertools

22 Context Manager

22 Context Manager

22.1 The with Statement

22.2 Helper Functions for with Contexts: contextlib

23 Decorators

23.1 Function Decorators

23.2 Class Decorators

23.3 The functools Module

24 Annotations for Static Type Checking

24 Annotations for Static Type Checking

24.1 Annotations

24.2 Type Hints: The typing Module

24.3 Static Type Checking in Python: mypy

25 Structural Pattern Matching

25 Structural Pattern Matching

25.1 The match Statement

25.2 Pattern Types in the case Statement

Part IV The Standard Library

Part IV The Standard Library

26 Mathematics

26.1 Mathematical Functions: math, cmath

26.2 Random Number Generator: random

26.3 Statistical Calculations: statistics

26.4 Intuitive Decimal Numbers: decimal

26.5 Hash Functions: hashlib

27 Screen Outputs and Logging

27 Screen Outputs and Logging

27.1 Formatted Output of Complex Objects: pprint

27.2 Log Files: logging

28 Regular Expressions

28 Regular Expressions

28.1 Syntax of Regular Expressions

28.2 Using the re Module

28.3 A Simple Sample Program: Searching

28.4 A More Complex Sample Program: Matching

28.5 Comments in Regular Expressions

29 Interface to Operating System and Runtime Environment

29 Interface to Operating System and Runtime Environment

29.1 Operating System Functionality: os

29.2 Accessing the Runtime Environment: sys

29.3 Command Line Parameters: argparse

30 File System

30.1 Accessing the File System: os

30.2 File Paths: os.path

30.3 Accessing the File System: shutil

30.4 Temporary Files: tempfile

31 Parallel Programming

31 Parallel Programming

31.1 Processes, Multitasking, and Threads

31.2 Python's Interfaces for Parallelization

31.3 The Abstract Interface: concurrent.futures

31.4 The Flexible Interface: threading and multiprocessing

31.5 Cooperative Multitasking

31.6 Conclusion: Which Interface Is the Right One?

32 Data Storage

32 Data Storage

32.3 Compressed Files and Archives

32.4 Serializing Instances: pickle

32.5 The JSON Data Exchange Format: json

32.6 The CSV Table Format: csv

33 Network Communication

33 Network Communication

33.1 Socket API

34 Accessing Resources on the Internet

34 Accessing Resources on the Internet

34.3 The Easy Way: requests

34.4 URLs: urllib

34.5 FTP: ftplib

35 Email

35.1 SMTP: smtplib

35.2 POP3: poplib

35.3 IMAP4: imaplib

35.4 Creating Complex Emails: email

36 Debugging and Quality Assurance

36 Debugging and Quality Assurance

36.1 The Debugger

36.2 Automated Testing

36.3 Analyzing the Runtime Performance

37 Documentation

37 Documentation

37.1 Docstrings

37.2 Automatically Generated Documentation: pydoc

Part V Advanced Topics

Part V Advanced Topics

38 Distributing Python Projects

38 Distributing Python Projects

38.1 A History of Distributions in Python

38.2 Creating Distributions: setuptools

38.3 Creating EXE files: cx_Freeze

38.4 Package Manager

38.5 Localizing Programs: gettext

39 Virtual Environments

39 Virtual Environments

39.1 Using Virtual Environments: venv

39.2 Virtual Environments in Anaconda

40 Alternative Interpreters and Compilers

40 Alternative Interpreters and Compilers

40.1 Just-in-Time Compilation: PyPy

40.3 Connecting to C and C++: Cython

40.4 The Interactive Python Shell: IPython

41 Graphical User Interfaces

41 Graphical User Interfaces

41.2 Introduction to tkinter

41.3 Introduction to PySide6

41.4 Signals and Slots

41.5 Important Widgets

41.6 Drawing Functionality

41.7 Model-View Architecture

42 Python as a Server-Side Programming Language on the Web: An Introduction to Django

42 Python as a Server-Side Programming Language on the Web: An Introduction to Django

42.1 Concepts and Features of Django

42.2 Installing Django

42.3 Creating a New Django Project

42.4 Creating an Application

43 Scientific Computing and Data Science

43 Scientific Computing and Data Science

43.1 Installation

43.2 The Model Program

43.3 Overview of the numpy and scipy Modules

43.4 An Introduction to Data Analysis with pandas

44 Inside Knowledge

44 Inside Knowledge

44.1 Opening URLs in the Default Browser: webbrowser

44.2 Interpreting Binary Data: struct

44.3 Hidden Password Entry

44.4 Command Line Interpreter

44.5 File Interface for Strings: io.StringIO

44.6 Generators as Consumers

44.7 Copying Instances: copy

44.8 Image Processing: Pillow

45 From Python 2 to Python 3

45 From Python 2 to Python 3

45.1 The Main Differences

45.2 Automatic Conversion

A Appendix

A.1 Reserved Words

A.2 Operator Precedence

A.3 Built-In Functions

A.4 Built-In Exceptions

A.5 Python IDEs

B The Authors

Index

Service Pages

Legal Notes

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About the 3 authors

Rheinwerk Publishing, Inc

Rheinwerk Publishing, Inc

The Rheinwerk Computing series offers new and established professional comprehensive guidance to enrich the readers' skillsets and enhance their career prospects. Their publications are written by leading experts in the fields of programming, administration, security, analytics, and more. Each book is detailed and hands-on to help readers develop essential, practical skills that they can apply to their daily work.

See other products by Rheinwerk Publishing, Inc

Johannes Ernesti

Johannes Ernesti

Johannes Ernesti is a research scientist at DeepL. He is a graduate mathematician and received his doctorate in applied mathematics from the Karlsruhe Institute of Technology (KIT).

See other products by Johannes Ernesti

Peter Kaiser

Peter Kaiser

Peter Kaiser is a research scientist at DeepL. He has a degree in computer science and received a doctorate in humanoid robotics from the Karlsruhe Institute of Technology (KIT).

See other products by Peter Kaiser

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