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Python Programming for Biology
Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.
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Unconventional Programming Paradigms
Nowadays, developers have to face the proliferation of hardware and software environments, the increasing demands of the users, the growing number of p- grams and the sharing of information, competences and services thanks to the generalization ofdatabasesandcommunication networks. Aprogramisnomore a monolithic entity conceived, produced and ?nalized before being used. A p- gram is now seen as an open and adaptive frame, which, for example, can - namically incorporate services not foreseen by the initial designer. These new needs call for new control structures and program interactions. Unconventionalapproachestoprogramminghavelongbeendevelopedinv- iousnichesandconstituteareservoirofalternativewaystofacetheprogramming languages crisis. New models of programming (e. g. , bio-inspired computing, - ti?cialchemistry,amorphouscomputing,. . . )arealsocurrentlyexperiencinga renewed period of growth as they face speci?c needs and new application - mains. These approaches provide new abstractions and notations or develop new ways of interacting with programs. They are implemented by embedding new sophisticated data structures in a classical programming model (API), by extending an existing language with new constructs (to handle concurrency, - ceptions, open environments, . . . ), by conceiving new software life cycles and program executions (aspect weaving, run-time compilation) or by relying on an entire new paradigm to specify a computation. They are inspired by theoretical considerations (e. g. , topological, algebraic or logical foundations), driven by the domain at hand (domain-speci?c languages like PostScript, musical notation, animation, signal processing, etc. ) or by metaphors taken from various areas (quantum computing, computing with molecules, information processing in - ological tissues, problem solving from nature, ethological and social modeling).
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Practical Maya Programming with Python
"Practical Maya Programming with Python" is a practical tutorial packed with plenty of examples and sample projects which guides you through building reusable, independent modules and handling unexpected errors. If you are a developer looking to build a powerful system using Python and Maya's capabilities, then this book is for you. Practical Maya Programming with Python is perfect for intermediate users with basic experience in Python and Maya who want to better their knowledge and skills.
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Python Using GPT-5 and Gemini
Python Using GPT-5 and Gemini provides a practical, up-to-date guide that combines solid Python programming skills with real-world integration of today’s leading large language models. The book teaches essential Python techniques while showing readers exactly how to connect and use GPT-5, Google Gemini, Claude, Grok, Perplexity, and other major models in their daily work.Readers will gain a strong foundation in Python data structures, comprehensions, generators, regular expressions, and object-oriented programming. The book then moves into detailed coverage of GPT-5 (including GPT-5.4 and GPT-5.3 Instant), Gemini 3.1, Claude Mythos and Opus 4.6, along with practical Python code samples for chatbots, tool calling, data analysis, graphics generation, and integration with PowerPoint and Excel. Additional chapters explore LLM benchmarks, deceptive behaviors such as alignment faking and scheming, military AI applications, and comparisons between the major models, giving readers clear insight into their relative strengths and limitations.This book is written for intermediate Python developers, data scientists, technical professionals, and anyone who wants to move beyond basic prompting and build production-ready applications that effectively combine Python with the latest generation of large language models.
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Django REST APIs Demystified
This book is designed to help you master API development using the Django REST Framework (DRF). Written for seasoned developers, this book offers practical, hands-on guidance to becoming an expert in using DRF, providing in-depth coverage of the framework's features and best practices and their application in API development. The book will introduce you to and enable you to grasp advanced DRF topics such as authentication, permissions, throttling, caching, API versioning, and schema generation. Each chapter of the book focuses on a specific topic and introduces a standalone Django application that will ensure a structured and isolated learning experience for you. Real-world examples and step-by-step instructions provide a practical approach to building DRF APIs. The book also aids you in exploring customizing DRF for complex use cases, integrating third-party tools, and writing unit tests for secure, robust APIs. Special attention is given to working with multi-application Django projects, offering conventions to avoid conflicts and ensure clear API documentation. By the end of this book, you will have the skills to build secure, scalable, and maintainable APIs using DRF. Whether you're working on a startup, enterprise project, or personal venture, this book equips you with the knowledge and tools to create efficient, future-proof APIs. What You Will Learn How to manage and structure complex multi-application projects without conflicts, leveraging best practicesOptimizing API Performance and Implementing advanced techniques like throttling, caching, and efficient query handling to enhance API performance.Use versioning, customization, and extensible patterns to create APIs that adapt to evolving business requirementsHow to manage and structure complex multi-application projects without conflicts, leveraging best practices. Who Is This Book For: Web developers, front-end engineers, full stack engineers, API documentation specialists, technical leads
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Python Network Programming
Power up your network applications with Python programmingKey FeaturesMaster Python skills to develop powerful network applicationsGrasp the fundamentals and functionalities of SDNDesign multi-threaded, event-driven architectures for echo and chat serversBook DescriptionThis Learning Path highlights major aspects of Python network programming such as writing simple networking clients, creating and deploying SDN and NFV systems, and extending your network with Mininet. You’ll also learn how to automate legacy and the latest network devices. As you progress through the chapters, you’ll use Python for DevOps and open source tools to test, secure, and analyze your network. Toward the end, you'll develop client-side applications, such as web API clients, email clients, SSH, and FTP, using socket programming.By the end of this Learning Path, you will have learned how to analyze a network's security vulnerabilities using advanced network packet capture and analysis techniques. This Learning Path includes content from the following Packt products:Practical Network Automation by Abhishek RatanMastering Python Networking by Eric ChouPython Network Programming Cookbook, Second Edition by Pradeeban Kathiravelu, Dr. M. O. Faruque SarkerWhat you will learnCreate socket-based networks with asynchronous modelsDevelop client apps for web APIs, including S3 Amazon and TwitterTalk to email and remote network servers with different protocolsIntegrate Python with Cisco, Juniper, and Arista eAPI for automationUse Telnet and SSH connections for remote system monitoringInteract with websites via XML-RPC, SOAP, and REST APIsBuild networks with Ryu, OpenDaylight, Floodlight, ONOS, and POXConfigure virtual networks in different deployment environmentsWho this book is forIf you are a Python developer or a system administrator who wants to start network programming, this Learning Path gets you a step closer to your goal. IT professionals and DevOps engineers who are new to managing network devices or those with minimal experience looking to expand their knowledge and skills in Python will also find this Learning Path useful. Although prior knowledge of networking is not required, some experience in Python programming will be helpful for a better understanding of the concepts in the Learning Path.
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Modern Python Cookbook
Complete recipes spread across 15 chapters to help you overcome commonly faced issues by Python for everybody across the globe. Each recipe takes a problem-solution approach to resolve for effective Python.Key FeaturesDevelop expressive and effective Python programsBest practices and common idioms through carefully explained recipesDiscover new ways to apply Python for data-focused developmentMake use of Python’s optional type annotationsBook DescriptionPython is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand.This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications.The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it.By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.What you will learnSee the intricate details of the Python syntax and how to use it to your advantageImprove your coding with Python readability through functionsManipulate data effectively using built-in data structuresGet acquainted with advanced programming techniques in PythonEquip yourself with functional and statistical programming featuresWrite proper tests to be sure a program works as advertisedIntegrate application software using PythonWho this book is forThe Python book is for web developers, programmers, enterprise programmers, engineers, and big data scientists. If you are a beginner, this book will get you started. If you are experienced, it will expand your knowledge base. A basic knowledge of programming would help.
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Python GUI Programming with Tkinter
Transform your evolving user requirements into feature-rich Tkinter applicationsKey FeaturesExtensively revised with new content on RESTful networking, classes in Tkinter, and the Notebook widgetTake advantage of Tkinter’s lightweight, portable, and easy-to-use featuresBuild better-organized code and learn to manage an evolving codebaseBook DescriptionTkinter is widely used to build GUIs in Python due to its simplicity. In this book, you’ll discover Tkinter’s strengths and overcome its challenges as you learn to develop fully featured GUI applications.Python GUI Programming with Tkinter, Second Edition, will not only provide you with a working knowledge of the Tkinter GUI library, but also a valuable set of skills that will enable you to plan, implement, and maintain larger applications. You’ll build a full-blown data entry application from scratch, learning how to grow and improve your code in response to continually changing user and business needs.You’ll develop a practical understanding of tools and techniques used to manage this evolving codebase and go beyond the default Tkinter widget capabilities. You’ll implement version control and unit testing, separation of concerns through the MVC design pattern, and object-oriented programming to organize your code more cleanly.You’ll also gain experience with technologies often used in workplace applications, such as SQL databases, network services, and data visualization libraries. Finally, you’ll package your application for wider distribution and tackle the challenge of maintaining cross-platform compatibility.What you will learnProduce well-organized, functional, and responsive GUI applicationsExtend the functionality of existing widgets using classes and OOPPlan wisely for the expansion of your app using MVC and version controlMake sure your app works as intended through widget validation and unit testingUse tools and processes to analyze and respond to user requestsBecome familiar with technologies used in workplace applications, including SQL, HTTP, Matplotlib, threading, and CSVUse PostgreSQL authentication to ensure data security for your applicationWho this book is forThis book is for programmers who understand the syntax of Python, but do not yet have the skills, techniques, and knowledge to design and implement a complete software application. A fair grasp of basic Python syntax is required.
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Programming Languages: Principles and Paradigms
This textbook is a thorough, up-to-date introduction to the principles and techniques that guide the design and implementation of modern programming languages. The goal of the book is to provide the basis for a critical understanding of most modern programming languages. Thus, rather than focusing on a specific language, the book identifies the most important principles shared by large classes of languages. The notion of ‘abstract machine’ is a unifying concept that helps to maintain an accurate and elementary treatment. The book introduces, analyses in depth, and compares the imperative, object-oriented, functional, logic, concurrent, constraint-based, and service-oriented programming paradigms. All material coming from the first English edition has been updated and extended, clarifying some tricky points, and discussing newer programming languages. This second edition contains new chapters dedicated to constraint, concurrent, and service-oriented programming.Topics and features:Requires familiarity with one programming language is a prerequisiteProvides a chapter on history offering context for most of the constructs in use todayPresents an elementary account of semantical approaches and of computabilityIntroduces new examples in modern programming languages like Python or ScalaOffers a chapter that opens a perspective on applications in artificial intelligenceConceived as a university textbook, this unique volume will also be suitable for IT specialists who want to deepen their knowledge of the mechanisms behind the languages they use. The choice of themes and the presentation style are largely influenced by the experience of teaching the content as part of a bachelor's degree in computer science.
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Python for the Life Sciences
Treat yourself to a lively, intuitive, and easy-to-follow introduction to computer programming in Python. The book was written specifically for biologists with little or no prior experience of writing code - with the goal of giving them not only a foundation in Python programming, but also the confidence and inspiration to start using Python in their own research. Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population. Best of all, Python for the Life Sciences shows you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing. If you are a life scientist interested in learning Python to jump-start your research, this is the book for you.What You'll Learn Write Python scripts to automate your lab calculationsSearch for important motifs in genome sequencesUse object-oriented programming with PythonStudy mining interaction network data for patternsReview dynamic modeling of biochemical switchesWho This Book Is ForLife scientists with little or no programming experience, including undergraduate and graduate students, postdoctoral researchers in academia and industry, medical professionals, and teachers/lecturers.“A comprehensive introduction to using Python for computational biology... A lovely book with humor and perspective” -- John Novembre, Associate Professor of Human Genetics, University of Chicago and MacArthur Fellow“Fun, entertaining, witty and darn useful. A magical portal to the big data revolution” -- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School“Alex and Gordon’s enthusiasm for Python is contagious” -- Glenys Thomson Professor of Integrative Biology, University of California, Berkeley
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Programming with Python for Social Scientists
As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including:the fundamentals of why and how to do your own programming in social scientific research,questions of ethics and research design,a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more.Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Mastering Financial Markets with Python
Master Technical Analysis in the Age of Python and Innovation Financial markets are evolving, and so should your strategies. Mastering Financial Markets with Python: New Horizons in Technical Analysis bridges the gap between traditional methods and the new era of data-driven analysis. This book equips you with the skills to design, implement, and evaluate advanced technical analysis techniques all through a practical, code-first approach. Inside, you will learn how to: Rethink and modernize classic indicators with Python. Build advanced volatility and moving average indicators. Detect and analyze harmonic and reversal patterns automatically. Detect and analyze timing patterns automatically. Apply enhanced Fibonacci techniques and alternative charting systems. Develop a new generation of technical indicators tailored to modern markets. This book provides hands-on tools to master today's fast-moving financial landscape. By combining innovation with Python, you'll gain a deeper understanding of markets and build smarter, more adaptive strategies. Why buy it now? Because trading edges are fleeting, and those who master Python-driven technical analysis will be the ones shaping the markets of tomorrow.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 3,079.26
