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"A guide to programming with Python, updated for Python 3, explores the fundamental theory behind design choices and offers a better understanding of Python implementation, covering such topics as locating performance bottlenecks, how Python abstracts the underlying computer architecture, and tools to compile Python down to machine code." -- Provided by publisher "Pythonの高速化技法を学ぶロングセラー書の改 訂版。待望のPython 3対応。本書ではCPUやメモリ使用量の観点からハ イパフォーマンスなコードを書くための考え方 や手法を解説します。そのために、パフォーマ ンスのボトルネックを測定する方法から、最適 なデータ構造の選択方法、CythonやNumbaなどのコ ンパイラの比較、非同期処理、マルチコアCPUの 活用法といった最適化のノウハウを、シンプル なサンプルプログラムを使って実際に効果を確 認しながら学びます。本書で学べる考え方や手 法はPython以外にも適用できるので、ハイパフォ ーマーを目指すプログラマーは必携の一冊です 。" -- Provided by publisher.

Detailed summary in vernacular field
不要浪费时间让Python屈就你在其他语言中学到 的模式。Python的简洁性有助于你迅速提升编程效 率，但这通常意味着你并未使用它所提供的所有 功能。《流畅的Python》是编程领域的实用经典参 考书，第2版做了与时俱进的修订和升级，教你利 用Python特性，写出高效且现代的Python 3代码。 打破旧有经验，探索并运用地道的Python 3特性。本书作者带你一览Python语言核心功能和库 ，教你编写更简洁、快速和易读的代码。 第2版分为如下五部分，每一部分均可单独成书。 数据结构：序列、字典、集合、Unicode和数据类。 函数即对象：一等函数、相关设计模式和函数声 明中的类型提示。 类和协议：组合、继承、混入、接口、运算符重 载、协议和更多静态类型。 控制流：上下文管理器、生成器、协程、async/awai t及线程和进程池。 元编程：特性、属性描述符、类装饰器，以及可 取代或简化元类的类元编程新钩子。

• table of contents PART 1: FOUNDATIONS READ IN LIVEBOOK 1THE WHAT AND WHY OF PYTHON PACKAGES
• READ IN LIVEBOOK 2PREPARING FOR PACKAGE DEVELOPMENT
• READ IN LIVEBOOK 3THE ANATOMY OF A MINIMAL PYTHON PACKAGE PART 2: CREATING A VIABLE PACKAGE READ IN LIVEBOOK 4HANDLING PACKAGE DEPENDENCIES, ENTRY POINTS, AND EXTENSIONS
• READ IN LIVEBOOK 5BUILDING AND MAINTAINING A TEST SUITE
• READ IN LIVEBOOK 6AUTOMATING CODE QUALITY TOOLING PART 3: GOING PUBLIC 7 AUTOMATING WORK THROUGH CONTINUOUS INTEGRATION
• 8 AUTHORING AND MAINTAINING DOCUMENTATION
• 9 MAKING A PACKAGE EVERGREEN PART 4: THE LONG HAUL 10 CREATING A REPEATABLE PROCESS
• 11 BUILDING AN OPEN SOURCE COMMUNITY APPENDIXES READ IN LIVEBOOK APPENDIX A: INSTALLING ASDF AND PYTHON-LAUNCHER
• READ IN LIVEBOOK APPENDIX B: INSTALLING PIPX, BUILD, AND TOX.
• (source: Nielsen Book Data)

Create Python packages to share your code in a scalable and maintainable way. Improve team productivity, publish helpful libraries, or even start your own open source project following the latest Python packaging standards. In Publishing Python Packages you will learn how to: Build extensions and console script commands Use tox to automate packaging, installing, and testing Build a continuous integration pipeline using GitHub Actions Improve code quality and reduce manual review using bandit, black, mypy, and radon Create published documentation for your packages Keep packages up to date with pyupgrade and Dependabot Foster an open source community using GitHub features Publishing Python Packages teaches you how to easily share your Python code with your team and the outside world. Learn a repeatable and highly automated process for package maintenance that's based on the best practices, tools, and standards of Python packaging. Whether you're entirely new to Python packaging or looking for optimal ways to maintain and scale your packages, this fast-paced and engaging guide is for you. about the technology Python packages are a great way to share your code and give a productivity boost to your colleagues and community. Whether you're reusing your code internally or contributing to open source, a properly automated system of packaging will save you from time-consuming manual maintenance. about the book Publishing Python Packages reveals best practices and standards for packaging your Python code in an easy, automated, and scalable way. The book walks you through creating a complete package, including a C extension, and guides you all the way to publishing on the Python Package Index. You'll get hands-on experience with the latest packaging tools, and learn the ins-and-outs of package testing and continuous integration. You'll even learn how to set up a successful open source project, including licensing, documentation, and nurturing a community of contributors.
(source: Nielsen Book Data)

Python was recently ranked as today's most popular programming language on the TIOBE index, thanks to its broad applicability to design and prototyping to testing, deployment, and maintenance. With this updated fourth edition, you'll learn how to get the most out of Python, whether you're a professional programmer or someone who needs this language to solve problems in a particular field. Carefully curated by recognized experts in Python, this new edition focuses on version 3.10, bringing this seminal work on the Python language fully up to date on five version releases, including preview coverage of upcoming 3.11 features. This handy guide will help you: Learn how Python represents data and program as objects Understand the value and uses of type annotations Examine which language features appeared in which recent versions Discover how to use modern Python idiomatically Learn ways to structure Python projects appropriately Understand how to debug Python code.
(source: Nielsen Book Data)

Dieses Buch ist für vielbeschäftigte Programmierer:innen, die eine knappe und dennoch gut verständliche Einführung in Python als immer populärer werdende Programmiersprache suchen. Python lernen – kurz & gut bietet einen unterhaltsamen Einstieg und informiert Sie über viele Python-Bestandteile, die Ihnen helfen werden, schnell durchzustarten:- Installation von Python- Schnelleinstieg in die wichtigsten Aspekte- Basisbausteine wie Strings, Enums, Zufallszahlen, Fallunterscheidungen und Schleifen- Klassen und objektorientierte Programmierung- Datencontainer wie Listen, Mengen und Tupel- Fortgeschrittene Themen zu Collections wie Iteratoren, Generatoren, Slicing, Sortierungen und Comprehensions- Datumsverarbeitung inklusive Berechnungen- Dateiverarbeitung und JSON sowie Behandlung von FehlernTrotz seines kompakten Formats liefert dieses Buch eine fundierte Einführung und eine Fülle an leicht nachvollziehbaren Beispielen, die zum Experimentieren einladen. Es unterstützt Sie optimal dabei, Ihre Python-Kenntnisse auf- und auszubauen. Insbesondere wenn Sie bereits ein wenig mit z.B. Java oder C++ vertraut sind, ist dieses Buch die ideale Wahl, um solide in Python einzusteigen.

Build and use the most popular time series index available today with Python to search and join time series at the subsequence level Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to implement algorithms and techniques from research papers Get to grips with building time series indexes using iSAX Leverage iSAX to solve real-world time series problems Book Description Time series are everywhere, ranging from financial data and system metrics to weather stations and medical records. Being able to access, search, and compare time series data quickly is essential, and this comprehensive guide enables you to do just that by helping you explore SAX representation and the most effective time series index, iSAX. The book begins by teaching you about the implementation of SAX representation in Python as well as the iSAX index, along with the required theory sourced from academic research papers. The chapters are filled with figures and plots to help you follow the presented topics and understand key concepts easily. But what makes this book really great is that it contains the right amount of knowledge about time series indexing using the right amount of theory and practice so that you can work with time series and develop time series indexes successfully. Additionally, the presented code can be easily ported to any other modern programming language, such as Swift, Java, C, C++, Ruby, Kotlin, Go, Rust, and JavaScript. By the end of this book, you'll have learned how to harness the power of iSAX and SAX representation to efficiently index and analyze time series data and will be equipped to develop your own time series indexes and effectively work with time series data. What you will learn Find out how to develop your own Python packages and write simple Python tests Understand what a time series index is and why it is useful Gain a theoretical and practical understanding of operating and creating time series indexes Discover how to use SAX representation and the iSAX index Find out how to search and compare time series Utilize iSAX visualizations to aid in the interpretation of complex or large time series Who this book is for This book is for practitioners, university students working with time series, researchers, and anyone looking to learn more about time series. Basic knowledge of UNIX, Linux, and Python and an understanding of basic programming concepts are needed to grasp the topics in this book. This book will also be handy for people who want to learn how to read research papers, learn from them, and implement their algorithms

• Chapter 1: What is Python.Goal: About Python, what it is, how to set up the interpreter on machineSub-topics Setting Up
• Chapter 2: The BasicsGoal: Covers basics, syntax, writing a basic program and executing the codeSub-topics Language ClassificationLow-Level LanguageHigh-Level LanguagePython Language SyntaxReserved WordsIdentifiersIndentationCommentsInputOutputEscape CharactersWriting a Program
• Chapter 3: Working with Data Goal: Covers data types: integers, lists, strings, etc, etc , variables, operatorsSub-topics VariablesLocal VariablesGlobal VariablesBasic Data TypesIntegersFloating Point NumbersStringsListsTwo Dimensional ListsSetsTuplesDictionariesCasting Data TypesArithmetic OperatorsOperator PrecedencePerforming ArithmeticComparison OperatorsBoolean OperatorsBitwise OperatorsLab Exercises
• Chapter 4: Flow ControlGoal: Explains flow control, sequence, if/elif, for/whileSub-topics SequenceSelectionif...elseelifIteration (Loops)For loopWhile loopBreak and ContinueLab Exercises
• Chapter 5: Handling FilesGoal: Explains file handling, reading files, writing to files, text files, binary files File TypesText FileBinaryText File OperationsOpen FilesWrite to a FileRead from a FileBinary File OperationsOpen FilesWrite to a FileRead a FileRandom File AccessLab Exercises
• Chapter 6: Using FunctionsSub-topics Declaring FunctionsRecursionLab Exercises
• Chapter 7: Exception HandlingGoal: Covers exception and error handling Sub-topicsTypes of ExceptionCatching ExceptionsRaising your Own Exceptions
• Chapter 8: Object Oriented ProgrammingGoal: OOP principles, classes, objects and inheritanceSub-topics Principles of OOPEncapsulationInheritancePolymorphismAbstractionClasses & ObjectsClass InheritancePolymorphic ClassesMethod Overriding
• Chapter 9: Building an InterfaceGoal: Building an interface using tkinterSub-topics Creating a WindowAdding WidgetsMenusThe CanvasImagesButtonsMessage BoxesText FieldListboxCheckboxLabelsLabel FrameInterface Design
• Chapter 10: Developing a GameSub-topics Installing PyGameOpening a WindowAdding an ImageThe Game LoopThe Event LoopShapesBasic Animation
• Chapter 11: Python Web DevelopmentSub-topics Web ServersExecuting a ScriptPython Web Frameworks Quick ReferenceData TypesNumeric OperatorsComparison OperatorsBoolean OperatorsString OperatorsList OperatorsDictionary OperatorsString MethodsList MethodsDictionary MethodsFunctionsFilesConditionalMulti ConditionalWhile LoopFor LoopLoop ControlModulesBuilt in FunctionsDeclare a ClassChild ClassCreate ObjectCall Object Method.
• (source: Nielsen Book Data)

Written as an illustrated, step-by-step guide, this book will introduce you to Python with examples using the latest version of the language. You'll begin by learning to set up your Python environment. The next few chapters cover the basics of Python such as language classifications, Python language syntax, and how to write a program. Next, you will learn how to work with variables, basic data types, arithmetic, companion, and Boolean operators, followed by lab exercises. Further, the book covers flow control, using functions, and exception handling, as well as the principles of object-oriented programming and building an interface design. The last section explains how to develop a game by installing PyGame and how to use basic animation, and concludes with coverage of Python web development with web servers and Python web frameworks. The Absolute Beginners Guide to Python Programming will give you the tools, confidence, and inspiration to start writing Python programs. If you are a programmer, developer, or a student, or someone who wants to learn on their own, this book is for you. What You Will Learn Gain an understanding of computer programming Understand different data and data types Work with Classes and OOP Build interfaces, simple games, and web development with Python Who This Book Is ForSoftware programmers, developers or students, or anyone who wants to learn Python programming on their own.
(source: Nielsen Book Data)

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You'll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you'll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.

• Table of Contents Getting Started - One Environment per Project Interactive Python Interpreters Pythonic Syntax and Common Pitfalls Pythonic Design Patterns Functional Programming - Readability Versus Brevity Decorators - Enabling Code Reuse by Decorating Generators and Coroutines - Infinity, One Step at a Time Metaclasses - Making Classes (Not Instances) Smarter Documentation - How to Use Sphinx and reStructuredText Testing and Logging - Preparing for Bugs Debugging - Solving the Bugs Performance - Tracking and Reducing Your Memory and CPU Usage asyncio - Multithreading without Threads Multiprocessing - When a Single CPU Core Is Not Enough Scientific Python and Plotting Artificial Intelligence Extensions in C/C++, System Calls, and C/C++ Libraries Packaging - Creating Your Own Libraries or Applications.
• (source: Nielsen Book Data)

Use advanced features of Python to write high-quality, readable code and packages Key Features Extensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive Python Shape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessing Learn how advanced Python features fit together to produce maintainable code Book DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges What you will learn Write beautiful Pythonic code and avoid common Python coding mistakes Apply the power of decorators, generators, coroutines, and metaclasses Use different testing systems like pytest, unittest, and doctest Track and optimize application performance for both memory and CPU usage Debug your applications with PDB, Werkzeug, and faulthandler Improve your performance through asyncio, multiprocessing, and distributed computing Explore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learn Extend Python's capabilities with C/C++ libraries and system calls Who this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary.
(source: Nielsen Book Data)

• Table of Contents The Need For and Applications of Meta programming Refresher of OOP Concepts in Python Understanding Decorators and Their Applications Working with Metaclasses Understanding Introspection Implementing Reflection on Python Objects Understanding Generics and Typing Defining Templates for Algorithms Understanding Code through Abstract Syntax Tree Understanding Method Resolution Order of Inheritance Creating Dynamic Objects Applying GOF Design Patterns -
• Part 1 Applying GOF Design Patterns -
• Part 2 Generating Code from AST Implementing a Case Study Following Best Practices.
• (source: Nielsen Book Data)

A practical approach to metaprogramming with real-world examples that enables the development of advanced frameworks, libraries, and applications using Python Key Features Learn applied metaprogramming through a simple step-by-step approach Work with easily understandable examples and explanations that take you deep into the theory of metaprogramming Get practical experience in writing reusable code with real-world examples Book DescriptionEffective and reusable code makes your application development process seamless and easily maintainable. With Python, you will have access to advanced metaprogramming features that you can use to build high-performing applications. The book starts by introducing you to the need and applications of metaprogramming, before navigating the fundamentals of object-oriented programming. Next, you will learn about simple decorators, work with metaclasses, and later focus on introspection and reflection. You'll also delve into generics and typing before defining templates for algorithms. As you progress, you will understand your code using abstract syntax trees and explore method resolution order. This Python book also shows you how to create your own dynamic objects before structuring the objects through design patterns. Finally, you will learn simple code-generation techniques along with discovering best practices and eventually building your own applications. By the end of this learning journey, you'll have acquired the skills and confidence you need to design and build reusable high-performing applications that can solve real-world problems. What you will learn Understand the programming paradigm of metaprogramming and its need Revisit the fundamentals of object-oriented programming Define decorators and work with metaclasses Employ introspection and reflection on your code Apply generics, typing, and templates to enhance your code Get to grips with the structure of your code through abstract syntax trees and the behavior through method resolution order Create dynamic objects and generate dynamic code Understand various design patterns and best practices Who this book is forIf you are an intermediate-level Python programmer looking to enhance your coding skills by developing reusable and advanced frameworks, then this book is for you. Basic knowledge of Python programming will help you get the most out of this learning journey.
(source: Nielsen Book Data)

• Cover
• Titel
• Impressum
• Inhalt
• Einleitung
• Kapitel 1: Grundlagen
• 1.1 Was ist Programmieren?
• 1.2 Hardware und Software
• 1.3 Programm als Algorithmus
• 1.4 Syntax und Semantik
• 1.5 Interpreter und Compiler
• 1.6 Programmierparadigmen
• 1.7 Objektorientierte Programmierung
• 1.7.1 Strukturelle Zerlegung
• 1.7.2 Die Welt als System von Objekten
• 1.7.3 Objekte besitzen Attribute und beherrschen Methoden
• 1.7.4 Objekte sind Instanzen von Klassen
• 1.8 Hintergrund: Geschichte der objektorientierten Programmierung
• 1.9 Aufgaben
• 1.10 Lösungen

• Kapitel 2: Der Einstieg
• Python im interaktiven Modus
• 2.1 Python installieren
• 2.2 Python im interaktiven Modus
• 2.2.1 Start des Python-Interpreters in einem Konsolenfenster
• 2.2.2 Die IDLE-Shell
• 2.2.3 Die ersten Python-Befehle ausprobieren
• 2.2.4 Hotkeys
• 2.3 Objekte
• 2.4 Namen
• 2.5 Hintergrund: Syntax-Regeln für Bezeichner
• 2.6 Schlüsselwörter
• 2.7 Anweisungen
• 2.7.1 Ausdruckanweisungen
• 2.7.2 Import-Anweisungen
• 2.7.3 Zuweisungen
• 2.7.4 Erweiterte Zuweisungen
• 2.7.5 Hintergrund: Dynamische Typisierung
• 2.8 Aufgaben
• 2.9 Lösungen

• Kapitel 3: Python-Skripte
• 3.1 Ausprobieren, nachmachen, besser machen!
• 3.2 Skripte editieren und ausführen mit IDLE
• 3.3 Ausführen eines Python-Skripts
• 3.4 Kommentare
• 3.5 Die Zeilenstruktur von Python-Programmen
• 3.6 Das EVA-Prinzip
• 3.7 Phasen der Programmentwicklung
• 3.8 Guter Programmierstil
• 3.9 Hintergrund: Die Kunst des Fehlerfindens
• 3.10 Weitere Entwicklungsumgebungen für Python
• 3.10.1 Thonny
• eine Entwicklungsumgebung für Python-Einsteiger
• 3.10.2 Python in der Cloud
• 3.10.3 Jupyter Notebook und Google Colab
• 3.10.4 Entwicklungsumgebungen für Profis

• 3.11 Aufgaben
• 3.12 Lösungen
• Kapitel 4: Standard-Datentypen
• 4.1 Daten als Objekte
• 4.2 Fundamentale Datentypen im Überblick
• 4.3 Typen und Klassen
• 4.4 NoneType
• 4.5 Wahrheitswerte
• der Datentyp bool
• 4.6 Ganze Zahlen
• 4.7 Gleitkommazahlen
• 4.8 Komplexe Zahlen
• 4.9 Arithmetische Operatoren für Zahlen
• 4.10 Sequenzen
• 4.10.1 Zeichenketten (Strings)
• 4.10.2 Bytestrings
• 4.10.3 Tupel
• 4.10.4 Liste
• 4.10.5 Bytearray
• 4.10.6 Einige Grundoperationen für Sequenzen
• 4.10.7 Veränderbare und unveränderbare Sequenzen
• 4.11 Mengen
• 4.12 Dictionaries

• 4.13 Typumwandlungen
• 4.13.1 int()
• 4.13.2 float()
• 4.13.3 complex()
• 4.13.4 bool()
• 4.13.5 str()
• 4.13.6 dict(), list() und tuple()
• 4.14 Aufgaben
• 4.15 Lösungen
• Kapitel 5: Kontrollstrukturen
• 5.1 Einfache Bedingungen
• 5.1.1 Vergleiche
• 5.1.2 Zugehörigkeit zu einer Menge (in, not in)
• 5.1.3 Beliebige Ausdrücke als Bedingungen
• 5.2 Zusammengesetzte Bedingungen
• logische Operatoren
• 5.2.1 Negation (not)
• 5.2.2 Konjunktion (and)
• 5.2.3 Disjunktion (or)
• 5.2.4 Formalisierung von Bedingungen
• 5.2.5 Hinweis zum Programmierstil

Die Skriptsprache Python ist mit ihrer einfachen Syntax hervorragend für Einsteiger geeignet, um modernes Programmieren zu lernen. Mit diesem Buch erhalten Sie einen umfassenden Einstieg in Python 3 und lernen darüber hinaus auch weiterführende Anwendungsmöglichkeiten kennen. Michael Weigend behandelt Python von Grund auf und erläutert die wesentlichen Sprachelemente. Er geht dabei besonders auf die Anwendung von Konzepten der objektorientierten Programmierung ein. Insgesamt liegt der Schwerpunkt auf der praktischen Arbeit mit Python. Ziel ist es, die wesentlichen Techniken und dahinterstehenden Ideen anhand zahlreicher anschaulicher Beispiele verständlich zu machen. Zu typischen Problemstellungen werden Schritt für Schritt Lösungen erarbeitet. So erlernen Sie praxisorientiert die Programmentwicklung mit Python und die Anwendung von Konzepten der objektorientierten Programmierung. Alle Kapitel enden mit einfachen und komplexen Übungsaufgaben mit vollständigen Musterlösungen. Das Buch behandelt die Grundlagen von Python 3 und zusätzlich auch weiterführende Themen wie die Gestaltung grafischer Benutzungsoberflächen mit tkinter und PyQt, Threads und Multiprocessing, Internet-Programmierung, CGI, WSGI und Django, automatisiertes Testen, Datenmodellierung mit XML und JSON, Datenbanken, Datenvisualisierung mit Matplotlib und wissenschaftliches Rechnen mit NumPy.

• Table of Contents Python Fundamentals - Math, Strings, Conditionals, and Loops Python Data Structures Executing Python - Programs, Algorithms, and Functions Extending Python, Files, Errors, and Graphs Constructing Python - Classes and Methods The Standard Library Becoming Pythonic Software Development Practical Python - Advance Topics Data Analytics with pandas and NumPy Machine Learning Deep Learning with Python New Features in Python.
• (source: Nielsen Book Data)

Gain proficiency, productivity, and power by working on projects and kick-starting your career in Python with this comprehensive, hands-on guide. Key Features Understand and utilize Python syntax, objects, methods, and best practices Explore Python's many features and libraries through real-world problems and big data Use your newly acquired Python skills in machine learning as well as web and software development Book DescriptionPython is among the most popular programming languages in the world. It's ideal for beginners because it's easy to read and write, and for developers, because it's widely available with a strong support community, extensive documentation, and phenomenal libraries - both built-in and user-contributed. This project-based course has been designed by a team of expert authors to get you up and running with Python. You'll work though engaging projects that'll enable you to leverage your newfound Python skills efficiently in technical jobs, personal projects, and job interviews. The book will help you gain an edge in data science, web development, and software development, preparing you to tackle real-world challenges in Python and pursue advanced topics on your own. Throughout the chapters, each component has been explicitly designed to engage and stimulate different parts of the brain so that you can retain and apply what you learn in the practical context with maximum impact. By completing the course from start to finish, you'll walk away feeling capable of tackling any real-world Python development problem. What you will learn Write efficient and concise functions using core Python methods and libraries Build classes to address different business needs Create visual graphs to communicate key data insights Organize big data and use machine learning to make regression and classification predictions Develop web pages and programs with Python tools and packages Automate essential tasks using Python scripts in real-time execution Who this book is forThis book is for professionals, students, and hobbyists who want to learn Python and apply it to solve challenging real-world problems. Although this is a beginner's course, you'll learn more easily if you already have an understanding of standard programming topics like variables, if-else statements, and functions. Experience with another object-oriented program, though not essential, will also be beneficial. If Python is your first attempt at computer programming, this book will help you understand the basics with adequate detail for a motivated student.
(source: Nielsen Book Data)

• Preface xiii
• Chapter 1: Python Basics 1 1.1 Running Python 1 1.2 Python Programs 2 1.3 Primitives, Variables, and Expressions 3 1.4 Arithmetic Operators 5 1.5 Conditionals and Control Flow 7 1.6 Text Strings 9 1.7 File Input and Output 12 1.8 Lists 13 1.9 Tuples 15 1.10 Sets 17 1.11 Dictionaries 18 1.12 Iteration and Looping 21 1.13 Functions 22 1.14 Exceptions 24 1.15 Program Termination 26 1.16 Objects and Classes 26 1.17 Modules 30 1.18 Script Writing 32 1.19 Packages 33 1.20 Structuring an Application 34 1.21 Managing Third-Party Packages 35 1.22 Python: It Fits Your Brain 36
• Chapter 2: Operators, Expressions, and Data Manipulation 37 2.1 Literals 37 2.2 Expressions and Locations 38 2.3 Standard Operators 39 2.4 In-Place Assignment 41 2.5 Object Comparison 42 2.6 Ordered Comparison Operators 42 2.7 Boolean Expressions and Truth Values 43 2.8 Conditional Expressions 44 2.9 Operations Involving Iterables 45 2.10 Operations on Sequences 47 2.11 Operations on Mutable Sequences 49 2.12 Operations on Sets 50 2.13 Operations on Mappings 51 2.14 List, Set, and Dictionary Comprehensions 52 2.15 Generator Expressions 54 2.16 The Attribute (.) Operator 56 2.17 The Function Call () Operator 56 2.18 Order of Evaluation 56 2.19 Final Words: The Secret Life of Data 58
• Chapter 3: Program Structure and Control Flow 59 3.1 Program Structure and Execution 59 3.2 Conditional Execution 59 3.3 Loops and Iteration 60 3.4 Exceptions 64 3.5 Context Managers and the with Statement 75 3.6 Assertions and __debug__ 77 3.7 Final Words 78
• Chapter 4: Objects, Types, and Protocols 79 4.1 Essential Concepts 79 4.2 Object Identity and Type 80 4.3 Reference Counting and Garbage Collection 81 4.4 References and Copies 83 4.5 Object Representation and Printing 84 4.6 First-Class Objects 85 4.7 Using None for Optional or Missing Data 87 4.8 Object Protocols and Data Abstraction 87 4.9 Object Protocol 89 4.10 Number Protocol 90 4.11 Comparison Protocol 92 4.12 Conversion Protocols 94 4.13 Container Protocol 95 4.14 Iteration Protocol 97 4.15 Attribute Protocol 98 4.16 Function Protocol 98 4.17 Context Manager Protocol 99 4.18 Final Words: On Being Pythonic 99
• Chapter 5: Functions 101 5.1 Function Definitions 101 5.2 Default Arguments 101 5.3 Variadic Arguments 102 5.4 Keyword Arguments 103 5.5 Variadic Keyword Arguments 104 5.6 Functions Accepting All Inputs 104 5.7 Positional-Only Arguments 105 5.8 Names, Documentation Strings, and Type Hints 106 5.9 Function Application and Parameter Passing 107 5.10 Return Values 109 5.11 Error Handling 110 5.12 Scoping Rules 111 5.13 Recursion 114 5.14 The lambda Expression 114 5.15 Higher-Order Functions 115 5.16 Argument Passing in Callback Functions 118 5.17 Returning Results from Callbacks 121 5.18 Decorators 124 5.19 Map, Filter, and Reduce 127 5.20 Function Introspection, Attributes, and Signatures 129 5.21 Environment Inspection 131 5.22 Dynamic Code Execution and Creation 133 5.23 Asynchronous Functions and await 135 5.24 Final Words: Thoughts on Functions and Composition 137
• Chapter 6: Generators 139 6.1 Generators and yield 139 6.2 Restartable Generators 142 6.3 Generator Delegation 142 6.4 Using Generators in Practice 144 6.5 Enhanced Generators and yield Expressions 146 6.6 Applications of Enhanced Generators 148 6.7 Generators and the Bridge to Awaiting 151 6.8 Final Words: A Brief History of Generators and Looking Forward 152
• Chapter 7: Classes and Object-Oriented Programming 153 7.1 Objects 153 7.2 The class Statement 154 7.3 Instances 155 7.4 Attribute Access 156 7.5 Scoping Rules 158 7.6 Operator Overloading and Protocols 159 7.7 Inheritance 160 7.8 Avoiding Inheritance via Composition 163 7.9 Avoiding Inheritance via Functions 166 7.10 Dynamic Binding and Duck Typing 167 7.11 The Danger of Inheriting from Built-in Types 167 7.12 Class Variables and Methods 169 7.13 Static Methods 173 7.14 A Word about Design Patterns 176 7.15 Data Encapsulation and Private Attributes 176 7.16 Type Hinting 179 7.17 Properties 180 7.18 Types, Interfaces, and Abstract Base Classes 183 7.19 Multiple Inheritance, Interfaces, and Mixins 187 7.20 Type-Based Dispatch 193 7.21 Class Decorators 194 7.22 Supervised Inheritance 197 7.23 The Object Life Cycle and Memory Management 199 7.24 Weak References 204 7.25 Internal Object Representation and Attribute Binding 206 7.26 Proxies, Wrappers, and Delegation 208 7.27 Reducing Memory Use with __slots__ 210 7.28 Descriptors 211 7.29 Class Definition Process 215 7.30 Dynamic Class Creation 216 7.31 Metaclasses 217 7.32 Built-in Objects for Instances and Classes 222 7.33 Final Words: Keep It Simple 223
• Chapter 8: Modules and Packages 225 8.1 Modules and the import Statement 225 8.2 Module Caching 227 8.3 Importing Selected Names from a Module 228 8.4 Circular Imports 230 8.5 Module Reloading and Unloading 232 8.6 Module Compilation 233 8.7 The Module Search Path 234 8.8 Execution as the Main Program 234 8.9 Packages 235 8.10 Imports Within a Package 237 8.11 Running a Package Submodule as a Script 238 8.12 Controlling the Package Namespace 239 8.13 Controlling Package Exports 240 8.14 Package Data 241 8.15 Module Objects 242 8.16 Deploying Python Packages 243 8.17 The Penultimate Word: Start with a Package 244 8.18 The Final Word: Keep It Simple 245
• Chapter 9: Input and Output 247 9.1 Data Representation 247 9.2 Text Encoding and Decoding 248 9.3 Text and Byte Formatting 250 9.4 Reading Command-Line Options 254 9.5 Environment Variables 256 9.6 Files and File Objects 256 9.7 I/O Abstraction Layers 260 9.8 Standard Input, Output, and Error 263 9.9 Directories 264 9.10 The print() function 265 9.11 Generating Output 265 9.12 Consuming Input 266 9.13 Object Serialization 268 9.14 Blocking Operations and Concurrency 269 9.15 Standard Library Modules 273 9.16 Final Words 296
• Chapter 10: Built-in Functions and Standard Library 297 10.1 Built-in Functions 297 10.2 Built-in Exceptions 314 10.3 Standard Library 318 10.4 Final Words: Use the Built-Ins 320
• Index 321.
• (source: Nielsen Book Data)

Expert Insight for Modern Python (3.6+) Development from the Author of Python Essential Reference The richness of modern Python challenges developers at all levels. How can programmers who are new to Python know where to begin without being overwhelmed? How can experienced Python developers know they're coding in a manner that is clear and effective? How does one make the jump from learning about individual features to thinking in Python at a deeper level? Dave Beazley's new Python Distilled addresses these and many other real-world issues. Focusing on Python 3.6 and higher, this concise handbook focuses on the essential core of the language, with updated code examples to illuminate how Python works and how to structure programs that can be more easily explained, tested, and debugged. Throughout, Beazley reflects all he's learned teaching Python to scientists, engineers, and developers, and pushing the envelope of what makes Python tick. Rather than trying to cover every possible feature and quirk of a 30-year-old language, this pragmatic guide provides a concise narrative related to fundamental programming topics such as data abstraction, control flow, program structure, functions, objects, and modules--topics that form the foundation for Python projects of any size. Explore Python's core, from variables to packages Solve data manipulation and analysis problems more effectively Structure programs with an eye towards clarity and reliability Control objects and master the "protocols" that define their behavior Master functions and functional programming idioms Discover the surprising power offered by generators Understand classes from both high-level and technical perspectives Plan for project growth by understanding modules and packages Learn techniques and abstractions for proper I/O handling Dicts!.
(source: Nielsen Book Data)

• Table of Contents Introduction to Clustering Hierarchical Clustering Neighborhood Approaches and DBSCAN An Introduction to Dimensionality Reduction and PCA Autoencoders t-Distributed Stochastic Neighbor Embedding (t-SNE) Topic Modeling Market Basket Analysis Hotspot Analysis.
• (source: Nielsen Book Data)

Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled data Key Features Learn how to select the most suitable Python library to solve your problem Compare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use them Delve into the applications of neural networks using real-world datasets Book DescriptionUnsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you'll learn what dimensionality reduction is and where to apply it. As you progress, you'll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python. What you will learn Understand the basics and importance of clustering Build k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packages Explore dimensionality reduction and its applications Use scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris dataset Employ Keras to build autoencoder models for the CIFAR-10 dataset Apply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction data Who this book is forThis course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
(source: Nielsen Book Data)

Detailed summary in vernacular field.
涵蓋Python 2.7&3.5 重點提示3.6新功能 快速參考指南 「本書不僅無所不包，Python有的書中都有，而且 容易理解。它清楚解釋了Python中每個部分存在的 理由，以及你應該用何種思維組合它們。」 --Peter Norvig Google研究總監 從設計和原型製作，到測試、部署和維護，用途 多樣的Python在當今最受歡迎的程式語言中始終名 列前茅。這本實用書籍的第三版為此語言提供了 快速的參考指南，包含Python 3.5、2.7，以及3.6 新功能的重點提示，介紹其龐大標準程式庫最常 用到的部分，還有一些好用的第三方模組與套件 。 適用於具有一些Python經驗或從其他程式語言而來 的程式設計師，本書涵蓋了廣泛的應用領域，包 括Web和網路程式設計、XML處理、資料庫互動，以 及高速的數值運算，並能幫助你了解Python如何結 合優雅性、簡潔性、實用性和純粹的力量來提供 獨特的功能組合。 本版涵蓋： ‧Python語法、物件導向的Python、標準程式庫模組 ，以及第三方的Python套件 ‧Python對檔案與文字作業、續存與資料庫、共時 執行，以及數值計算的支援 ‧網路基本知識、事件驅動程式設計，以及客戶 端網路協定模組 ‧Python擴充模組，以及用於封裝和發布擴充功能 、模組與應用程式的工具.

This book is an introduction to programming concepts that uses Python 3 as the target language. It follows a practical just in time presentation - material is given to the student when it is needed. Many examples will be based on games, because Python has become the language of choice for basic game development. Designed as a year 1 textbook for introduction to programming classes or for the hobbyist who wants to learn the fundamentals of programming, flee text dreamer no programming experience.
(source: Nielsen Book Data)

• 1. Introduction
• 2. The core Python language I
• 3. Interlude: simple plotting with pylab
• 4. The core Python language II
• 5. IPython and IPython notebook
• 6. NumPy
• 7. Matplotlib
• 8. SciPy
• 9. General scientific programming
• Appendix A. Solutions
• Index.
• (source: Nielsen Book Data)

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming.
(source: Nielsen Book Data)