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Simulation with Python
Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.What You'll LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds.
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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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Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
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Test-Driven Development with Python
The third edition of this trusted guide demonstrates the practical advantages of test-driven development (TDD) with Python and describes how to develop a real web application. You'll learn how to write and run tests before building each part of your app and then develop the minimum amount of code required to pass those tests. The result? Clean code that works. In the process, author Harry Percival teaches software and web developers the basics of Django, Selenium, Git, JavaScript, and Mock libraries, along with current web development techniques. This book--updated for Python 3.14 and Django 4--clearly demonstrates how TDD encourages simple designs and inspires confidence. Fully updated, this third edition addresses: The TDD workflow, including the unit test/code cycle and refactoring Unit tests for classes and functions and functional tests for user interactions within the browser Mock objects and the pros and cons of isolated versus integrated tests Testing and automation of deployments with a staging server Tests applied to the third-party plug-ins you integrate into your site Automatic tests using a continuous integration environment Using TDD to build a REST API with a JavaScript frontend interface
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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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Handbook of Computer Programming with Python
This handbook provides a hands-on experience based on the underlying topics, and assists students and faculty members in developing their algorithmic thought process and programs for given computational problems. It can also be used by professionals who possess the necessary theoretical and computational thinking background but are presently making their transition to Python.Key Features:Discusses concepts such as basic programming principles, OOP principles, database programming, GUI programming, application development, data analytics and visualization, statistical analysis, virtual reality, data structures and algorithms, machine learning, and deep learning Provides the code and the output for all the concepts discussedIncludes a case study at the end of each chapterThis handbook will benefit students of computer science, information systems, and information technology, or anyone who is involved in computer programming (entry-to-intermediate level), data analytics, HCI-GUI, and related disciplines.
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Artificial Intelligence with Python
New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more.Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook DescriptionArtificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is forThe intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
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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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101 Extra Python Challenges with Solutions / Code Listings
Welcome to this second selection of 101 Python challenges from the 101computing.net website. As its predecessor - 101 Python Challenges with solutions / code listing, published in 2017 - the aim of this follow up book is to help you develop and apply your programing skills by solving carefully selected challenges. The book is organised into ten chapters to progressively introduce a range of key procedural programming concepts.
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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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Python GUI Programming Cookbook
Over 90 recipes to help you develop widgets, forms, layouts, charts, and much more using the latest features of Python 3Key FeaturesUse object-oriented programming to develop impressive GUIs in PythonCreate interesting charts to visually represent data using MatplotlibDevelop GUIs with the latest versions of tkinter, PyQt5, and wxPython frameworksBook DescriptionPython is a multi-domain, interpreted programming language that is easy to learn and implement. With its wide support for frameworks to develop GUIs, you can build interactive and beautiful GUI-based applications easily using Python. This third edition of Python GUI Programming Cookbook follows a task-based approach to help you create effective GUIs with the smallest amount of code. Every recipe in this book builds upon the last to create an entire, real-life GUI application. These recipes also help you solve problems that you might encounter while developing GUIs. This book mainly focuses on using Python’s built-in tkinter GUI framework. You'll learn how to create GUIs in Python using simple programming styles and object-oriented programming (OOP). As you add more widgets and expand your GUI, you will learn how to connect to networks, databases, and graphical libraries that greatly enhance the functionality of your GUI. You’ll also learn how to use threading to ensure that your GUI doesn't become unresponsive. Toward the end, you’ll learn about the versatile PyQt GUI framework, which comes along with its own visual editor that allows you to design GUIs using drag and drop features. By the end of the book, you’ll be an expert in designing Python GUIs and be able to develop a variety of GUI applications with ease.What you will learnCreate amazing GUIs with Python's built-in tkinter moduleCustomize GUIs using layout managers to arrange GUI widgetsAdvance from the typical waterfall coding style to an OOP style using PythonDevelop beautiful charts using the free Matplotlib Python moduleUse threading in a networked environment to make GUIs responsiveDiscover ways to connect GUIs to a MySQL databaseUnderstand how unit tests can be created and internationalize GUIDelve into the world of GUI creation using PyQt5Who this book is forIf you’re a programmer or developer looking to enhance your Python skills by writing powerful GUI applications, this book is for you. Familiarity with the Python programming language is necessary to get the most out of the book.
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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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Gray Hat Python
Python is fast becoming the programming language of choice for hackers, reverse engineers, and software testers because it's easy to write quickly, and it has the low-level support and libraries that make hackers happy. But until now, there has been no real manual on how to use Python for a variety of hacking tasks. You had to dig through forum posts and man pages, endlessly tweaking your own code to get everything working. Not anymore. Gray Hat Python explains the concepts behind hacking tools and techniques like debuggers, trojans, fuzzers, and emulators. But author Justin Seitz goes beyond theory, showing you how to harness existing Python-based security tools and how to build your own when the pre-built ones won't cut it. You'll learn how to: Automate tedious reversing and security tasks Design and program your own debugger Learn how to fuzz Windows drivers and create powerful fuzzers from scratch Have fun with code and library injection, soft and hard hooking techniques, and other software trickery Sniff secure traffic out of an encrypted web browser session Use PyDBG, Immunity Debugger, Sulley, IDAPython, PyEMU, and more The world's best hackers are using Python to do their handiwork. Shouldn't you?
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Building Machine Learning Systems with Python -
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
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Hands-On Blockchain for Python Developers
Write popular DeFi and NFT smart contracts with Vyper, a Pythonic programming language, and integrate blockchain with real-world applications using Python Key FeaturesUse the world's easiest programming language to build web3 applicationsWrite common smart contracts like decentralized exchanges, NFT marketplaces, and lending applicationsUnlock deeper levels of insights with technologies relating to blockchain, such as IPFS and Layer 2Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionWe are living in the age of decentralized fi nance and NFTs. People swap tokens on Uniswap, borrow assets from Aave, send payments with stablecoins, trade art NFTs on OpenSea, and more. To build applications of this kind, you need to know how to write smart contracts.This comprehensive guide will help you explore all the features of Vyper, a programming language designed to write smart contracts. You’ll also explore the web3.py library. As you progress, you’ll learn how to connect to smart contracts, read values, and create transactions. To make sure your foundational knowledge is strong enough, the book guides you through Ape Framework, which can help you create decentralized exchanges, NFT marketplaces, voting applications, and more. Each project provides invaluable insights and hands-on experience, equipping you with the skills you need to build real-world blockchain solutions.By the end of this book, you’ll be well versed with writing common Web3 applications such as a decentralized exchange, an NFT marketplace, a voting application, and more.What you will learnUnderstand blockchain and smart contractsLearn how to write smart contracts with VyperExplore how to use the web3.py library and Ape FrameworkDiscover related technologies such as Layer 2 and IPFSGain a step-by-step guide to writing an automated market maker (AMM) decentralized exchange (DEX) smart contractBuild innovative, interactive, and token-gated Web3 NFT applicationsWho this book is forThis blockchain book is for developers interested in understanding blockchain and smart contracts. It is suitable for both technology enthusiasts looking to explore blockchain technology and programmers who aspire to become smart contract engineers. Basic knowledge of GNU/Linux and Python programming is mandatory to get started with this book.
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Paradigms of Artificial Intelligence Programming
Paradigms of AI Programming is the first text to teach advanced Common Lisp techniques in the context of building major AI systems. By reconstructing authentic, complex AI programs using state-of-the-art Common Lisp, the book teaches students and professionals how to build and debug robust practical programs, while demonstrating superior programming style and important AI concepts. The author strongly emphasizes the practical performance issues involved in writing real working programs of significant size. Chapters on troubleshooting and efficiency are included, along with a discussion of the fundamentals of object-oriented programming and a description of the main CLOS functions. This volume is an excellent text for a course on AI programming, a useful supplement for general AI courses and an indispensable reference for the professional programmer.
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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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