The Ultimate Python Development Bundle by Packt

Get to grips with the Model Context Protocol (MCP) and learn how to build scalable, intelligent web applications with AI-ready architecture in Python Key Features The only resource you'll need to build, test, and deploy MCP servers and clients Take a modern approach toward building, testing, and securing distributed agentic AI apps Get clear, professional guidance on developing for both LLM and non-LLM clients Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionLearn Model Context Protocol with Python introduces developers, architects, and AI practitioners to the transformative capabilities of Model Context Protocol (MCP), an emerging protocol designed to standardize, distribute, and scale AI-driven applications. Through the lens of a practical project, the book tackles the modern challenges of resource management, client-server interaction, and deployment at scale. Drawing from Christoffer's expertise as a published author and tutor at the University of Oxford, you’ll explore the components of MCP and how they streamline server and client development. Next, you’ll progress from building robust backends and integrating LLMs into intelligent clients to interacting with servers via tools such as Claude for desktop and Visual Studio Code agents. The chapters help you understand how to describe the capabilities of hosts, clients, and servers, facilitating better interoperability, easier integration, and clearer communication between different components. The book also covers security best practices and building for the cloud, ensuring that you're ready to deploy your MCP-based apps. Each chapter enables you to develop hands-on skills for building and operating MCP-based agentic apps. The Python primer at the end rounds out the practical toolkit, making this book essential for any team building AI-native applications today.What you will learn Understand the MCP protocol and its core components Build MCP servers that expose tools and resources to a variety of clients Test and debug servers using the interactive inspector tools Consume servers using Claude Desktop and Visual Studio Code Agents Secure MCP apps, as well as managing and mitigating common threats Build and deploy MCP apps using cloud-based strategies Who this book is for This book is ideal for web developers, software architects, AI practitioners, and tech leads looking to build scalable AI-integrated applications using MCP. Product managers overseeing AI initiatives will also find this book valuable. A basic understanding of web development and AI concepts is needed to make the most of this book.

This hands-on guide empowers Python developers to write clean, production-ready code using real-world OOP, design patterns, and concurrency tools. Fully updated for Python 3.13 with new type hinting and modern software engineering practices. Key Features: - Master OOP fundamentals with hands-on examples and expert insights - Learn design patterns and type hinting with real-world Python 3.13 code - Develop scalable programs using testing and concurrency best practices - Purchase of the print or Kindle book includes a free PDF eBook Book Description: Learn to write effective, maintainable, and scalable Python applications by mastering object-oriented programming with this updated fifth edition. Whether you're transitioning from scripting to structured development or refining your OOP skills, this book offers a clear, practical path forward. You'll explore Python's approach to OOP, from class creation and inheritance to polymorphism and abstraction, while discovering how to make smarter decisions about when and how to use these tools. You'll apply what you learn through hands-on examples and exercises. Updated for Python 3.13, this edition simplifies complex topics such as abstract base classes, testing with unittest and pytest, and async programming with asyncio. It introduces a new chapter on Python's type hinting ecosystem-crucial for modern Python development. Written by long-time Python experts Steven Lott and Dusty Phillips, this edition emphasizes clarity, testability, and professional software engineering practices. It helps you move beyond scripting to building well-structured, production-ready Python systems. By the end of this book, you'll be confident in applying OOP principles, design patterns, type hints, and concurrency tools to create robust and maintainable Python applications. What You Will Learn: - Write Python classes and implement object behaviors - Apply inheritance, polymorphism, and composition - Understand when to use OOP-and when not to - Use type hints and perform static and runtime checks - Explore common and advanced design patterns in Python - Write unit and integration tests with unittest and pytest - Implement concurrency with asyncio, futures, and threads - Refactor procedural code into well-designed OOP structures Who this book is for: Python developers who want to deepen their understanding of object-oriented programming to write maintainable, scalable, and professional-grade code. Ideal for developers transitioning from scripting to software engineering or those coming from other OOP languages looking to master Python's idiomatic approach. Basic Python knowledge is required. Table of Contents - Object-Oriented Design - Objects in Python - When Objects Are Alike - Expecting the Unexpected - When to Use Object-Oriented Programming - Abstract Base Classes and Operator Overloading - Python Type Hints - Python Data Structures - The Intersection of Object-Oriented and Functional Programming - The Iterator Pattern - Common Design Patterns - Advanced Design Patterns - Testing Object-Oriented Programs - Concurrency

Take a practical journey through the core principles, concepts, and applications of asynchronous programming in Python across real-world environments and technologies. Key Features Understand core principles and theory behind async programming in Python Measure the impact of async techniques in practical, real-world use cases Apply async patterns in software design and data-oriented architectures Book DescriptionAsynchronous programming is one of the most effective but often misunderstood techniques for building fast, scalable, and responsive systems in Python. While it can significantly improve performance, efficiency, and sustainability, using async without a clear understanding of its trade-offs can lead to fragile designs and hard-to-debug issues. This book offers a structured approach to applying asynchronous programming in Python. It begins with a conceptual framework to help you distinguish between synchronous and asynchronous execution models, and shows how async relates to other concurrency strategies such as multithreading and multiprocessing. From there, you will explore the core tools available for building async applications in Python. You will also learn how to measure the impact of async programming in practical scenarios, profile and debug asynchronous code, and evaluate performance improvements using real-world metrics. The final chapters focus on applying async techniques to common cloud-based systems, such as web frameworks, database interactions, and data-pipelines tools. Designed for developers looking to apply async programming with confidence, this book blends real-world examples with core concepts to help you write efficient, maintainable Python code.What you will learn Use generators, coroutines and async/await to build scalable Python functions Explore event loops to manage concurrency and orchestrate async flow Compare concurrency models to choose the right async strategy Optimize I/O-intensive programs to improve system throughput and efficiency Build async services using real-world APIs and popular Python libraries Apply structured concurrency and design patterns for cleaner async design Test and debug async Python code to ensure reliability and stability Who this book is for This book will help Python developers who want to understand and apply the asynchronous programming model in application development, data analysis, and orchestration scenarios. Junior developers, data engineers,, and tech leads will also benefit from the application design examples.

Accelerate your Python code on the GPU using CUDA, Numba, and modern libraries to solve real-world problems faster and more efficiently. Key Features Build a solid foundation in CUDA with Python, from kernel design to execution and debugging Optimize GPU performance with efficient memory access, CUDA streams, and multi-GPU scaling Use JAX, CuPy, RAPIDS, and Numba to accelerate numerical computing and machine learning Create practical GPU applications, from PDE solvers to image processing and transformers Book DescriptionWriting high-performance Python code doesn’t have to mean switching to C++. This book shows you how to accelerate Python applications using NVIDIA’s CUDA platform and a modern ecosystem of Python tools and libraries. Aimed at researchers, engineers, and data scientists, it offers a practical yet deep understanding of GPU programming and how to fully exploit modern GPU hardware. You’ll begin with the fundamentals of CUDA programming in Python using Numba-CUDA, learning how GPUs work and how to write, execute, and debug custom GPU kernels. Building on this foundation, the book explores memory access optimization, asynchronous execution with CUDA streams, and multi-GPU scaling using Dask-CUDA. Performance analysis and tuning are emphasized throughout, using NVIDIA Nsight profilers. You’ll also learn to use high-level GPU libraries such as JAX, CuPy, and RAPIDS to accelerate numerical Python workflows with minimal code changes. These techniques are applied to real-world examples, including PDE solvers, image processing, physical simulations, and transformer models. Written by experienced GPU practitioners, this hands-on guide emphasizes reproducible workflows using Python 3.10+, CUDA 12.3+, and tools like the Pixi package manager. By the end, you’ll have future-ready skills for building scalable GPU applications in Python.What you will learn Understand GPU execution, parallelism, and the CUDA programming model Write, launch, and debug custom CUDA kernels in Python with CUDA Profile GPU code with NVIDIA Nsight and optimize memory access Use CUDA streams and async execution to overlap compute and transfers Apply JAX, CuPy, and RAPIDS to numerical computing and machine learning Scale GPU workloads across devices using Dask and multi-GPU strategies Accelerate PDE solvers, simulations, and image processing on the GPU Build, train, and run a transformer model from scratch on the GPU Who this book is for Python developers, (data) scientists, engineers, and researchers looking to accelerate numerical computations without switching to low-level languages. This book is ideal for those with experience in scientific Python (NumPy, Pandas, SciPy) and a basic understanding of computing fundamentals who want deeper control over performance in GPU environments.

Become an expert in implementing advanced, network-related tasks with Python. Key Features Build the skills to perform all networking tasks using Python with ease Use Python for network device automation, DevOps, and software-defined networking Get practical guidance to networking with Python Book DescriptionThis book begins with a review of the TCP/ IP protocol suite and a refresher of the core elements of the Python language. Next, you will start using Python and supported libraries to automate network tasks from the current major network vendors. We will look at automating traditional network devices based on the command-line interface, as well as newer devices with API support, with hands-on labs. We will then learn the concepts and practical use cases of the Ansible framework in order to achieve your network goals. We will then move on to using Python for DevOps, starting with using open source tools to test, secure, and analyze your network. Then, we will focus on network monitoring and visualization. We will learn how to retrieve network information using a polling mechanism, ?ow-based monitoring, and visualizing the data programmatically. Next, we will learn how to use the Python framework to build your own customized network web services. In the last module, you will use Python for SDN, where you will use a Python-based controller with OpenFlow in a hands-on lab to learn its concepts and applications. We will compare and contrast OpenFlow, OpenStack, OpenDaylight, and NFV. Finally, you will use everything you’ve learned in the book to construct a migration plan to go from a legacy to a scalable SDN-based network.What you will learn Review all the fundamentals of Python and the TCP/IP suite Use Python to execute commands when the device does not support the API or programmatic interaction with the device Implement automation techniques by integrating Python with Cisco, Juniper, and Arista eAPI Integrate Ansible using Python to control Cisco, Juniper, and Arista networks Achieve network security with Python Build Flask-based web-service APIs with Python Construct a Python-based migration plan from a legacy to scalable SDN-based network Who this book is for If you are a network engineer or a programmer who wants to use Python for networking, then this book is for you. A basic familiarity with networking-related concepts such as TCP/IP and a familiarity with Python programming will be useful.

Get up and running with Python 3.9 through concise tutorials and practical projects in this fully updated third edition. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Extensively revised with richer examples, Python 3.9 syntax, and new chapters on APIs and packaging and distributing Python code Discover how to think like a Python programmer Learn the fundamentals of Python through real-world projects in API development, GUI programming, and data science Book DescriptionLearn Python Programming, Third Edition is both a theoretical and practical introduction to Python, an extremely flexible and powerful programming language that can be applied to many disciplines. This book will make learning Python easy and give you a thorough understanding of the language. You'll learn how to write programs, build modern APIs, and work with data by using renowned Python data science libraries. This revised edition covers the latest updates on API management, packaging applications, and testing. There is also broader coverage of context managers and an updated data science chapter. The book empowers you to take ownership of writing your software and become independent in fetching the resources you need. You will have a clear idea of where to go and how to build on what you have learned from the book. Through examples, the book explores a wide range of applications and concludes by building real-world Python projects based on the concepts you have learned.What you will learn Get Python up and running on Windows, Mac, and Linux Write elegant, reusable, and efficient code in any situation Avoid common pitfalls like duplication, complicated design, and over-engineering Understand when to use the functional or object-oriented approach to programming Build a simple API with FastAPI and program GUI applications with Tkinter Get an initial overview of more complex topics such as data persistence and cryptography Fetch, clean, and manipulate data, making efficient use of Python's built-in data structures Who this book is for This book is for everyone who wants to learn Python from scratch, as well as experienced programmers looking for a reference book. Prior knowledge of basic programming concepts will help you follow along, but it’s not a prerequisite.

Automate vulnerability scanning, network monitoring, and web application security using Python scripts, while exploring real-world case studies and emerging trends like AI and ML in security automation Key Features Gain future-focused insights into using machine learning and AI for automating threat detection and response Get a thorough understanding of Python essentials, tailored for security professionals Discover real-world applications of Python automation for enhanced security Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDesigned to address the most common pain point for security teams—scalability—Security Automation with Python leverages the author’s years of experience in vulnerability management to provide you with actionable guidance on automating security workflows to streamline your operations and improve your organization’s overall security posture. What makes this book stand out is its hands-on approach. You won’t just learn theoretical concepts—you’ll apply Python-based automation techniques directly to real-world scenarios. Whether you're automating vulnerability scans, managing firewall rules, or responding to security incidents, this book provides clear examples and use cases, breaking down complex topics into easily digestible steps. With libraries like Paramiko, Requests, and PyAutoGUI, you’ll automate everything from network scanning and threat intelligence gathering to system patching and alert management. Plus, this book focuses heavily on practical tips for error handling, scaling automation workflows, and integrating Python scripts into larger security infrastructures. By the end of this book, you'll have developed a set of highly valuable skills, from creating custom automation scripts to deploying them in production environments, and completed projects that can be immediately put to use in your organization.What you will learn Use Python libraries to automate vulnerability scans and generate detailed reports Integrate Python with security tools like Nessus to streamline SecOps Write custom Python scripts to perform security-related tasks Automate patch management to reduce the risk of security breaches Enhance threat intelligence gathering and improve your proactive defense strategies Scale security automation workflows for large environments Implement best practices for error handling, logging, and optimizing workflows Incorporate automation into security frameworks like NIST 800-53 and FedRAMP Who this book is for This book is for cybersecurity professionals, security analysts, system administrators, and developers looking to leverage Python to automate and enhance their security operations. Whether you're new to Python or experienced in scripting, the book provides practical examples, real-world case studies, and future-focused insights into security automation trends.

Improve your software engineering practices to tackle inefficiencies, errors, and other perils that emerge due to bad code Key Features Enhance your coding skills to increase efficiency as well as reflect the new features introduced in Python 3.9 Understand how to apply microservices to your legacy systems by implementing practical techniques Learn to implement the refactoring techniques and SOLID principles in Python Book Description The Python language is immensely prevalent in numerous areas such as software construction, systems administration, and data processing. Experienced professionals in every field face the challenges of disorganization, poor readability, and testability due to unstructured code. With updated code and revised content aligned to the new features of Python 3.9, this second edition of Clean code in Python will provide you with all the tools you need to overcome these obstacles and manage your projects successfully. The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. The book delves more deeply into object-oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve problems by implementing software design patterns in your code. In the final chapter, we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform. By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable and readable real-world Python code. What You Will Learn Set up a productive development environment by leveraging automatic tools Leverage the magic methods in Python to write better code, abstracting complexity away and encapsulating details Create advanced object-oriented designs using unique features of Python, such as descriptors Eliminate duplicated code by creating powerful abstractions using software engineering principles of object-oriented design Create Python-specific solutions using decorators and descriptors Refactor code effectively with the help of unit tests Build the foundations for solid architecture with a clean code base as its cornerstone Who this book is for This book will appeal to team leads, software architects, and senior software engineers who want to learn Python coding techniques and work on their legacy systems to save cost and improve efficiency. The book assumes that you have a strong understanding of programming.

Master GenAI techniques to create images and text using variational autoencoders (VAEs), generative adversarial networks (GANs), LSTMs, and large language models (LLMs) Key Features Implement real-world applications of LLMs and generative AI Fine-tune models with PEFT and LoRA to speed up training Expand your LLM toolbox with Retrieval Augmented Generation (RAG) techniques, LangChain, and LlamaIndex Purchase of the print or Kindle book includes a free eBook in PDF format Book Description Become an expert in Generative AI through immersive, hands-on projects that leverage today’s most powerful models for Natural Language Processing (NLP) and computer vision. Generative AI with Python and PyTorch is your end-to-end guide to creating advanced AI applications, made easy by Raghav Bali, a seasoned data scientist with multiple patents in AI, and Joseph Babcock, a PhD and machine learning expert. Through business-tested approaches, this book simplifies complex GenAI concepts, making learning both accessible and immediately applicable. From NLP to image generation, this second edition explores practical applications and the underlying theories that power these technologies. By integrating the latest advancements in LLMs, it prepares you to design and implement powerful AI systems that transform data into actionable intelligence. You’ll build your versatile LLM toolkit by gaining expertise in GPT-4, LangChain, RLHF, LoRA, RAG, and more. You’ll also explore deep learning techniques for image generation and apply styler transfer using GANs, before advancing to implement CLIP and diffusion models. Whether you’re generating dynamic content or developing complex AI-driven solutions, this book equips you with everything you need to harness the full transformative power of Python and AI. What will you learn Grasp the core concepts and capabilities of LLMs Craft effective prompts using chain-of-thought, ReAct, and prompt query language to guide LLMs toward your desired outputs Understand how attention and transformers have changed NLP Optimize your diffusion models by combining them with VAEs Build text generation pipelines based on LSTMs and LLMs Leverage the power of open-source LLMs, such as Llama and Mistral, for diverse applications Who this book is for This book is for data scientists, machine learning engineers, and software developers seeking practical skills in building generative AI systems. A basic understanding of math and statistics and experience with Python coding is required.

Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more Discover modern causal inference techniques for average and heterogenous treatment effect estimation Explore and leverage traditional and modern causal discovery methods Book DescriptionCausal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.What you will learn Master the fundamental concepts of causal inference Decipher the mysteries of structural causal models Unleash the power of the 4-step causal inference process in Python Explore advanced uplift modeling techniques Unlock the secrets of modern causal discovery using Python Use causal inference for social impact and community benefit Who this book is for This book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.

Grow your software engineering discipline, incorporating and mastering design, development, testing, and deployment best practices examples in a realistic Python project structure. Key Features Understand what makes Software Engineering a discipline, distinct from basic programming Gain practical insight into updating, refactoring, and scaling an existing Python system Implement robust testing, CI/CD pipelines, and cloud-ready architecture decisions Book Description Software engineering is more than coding; it’s the strategic design and continuous improvement of systems that serve real-world needs. This newly updated second edition of Hands-On Software Engineering with Python expands on its foundational approach to help you grow into a senior or staff-level engineering role. Fully revised for today’s Python ecosystem, this edition includes updated tooling, practices, and architectural patterns. You’ll explore key changes across five minor Python versions, examine new features like dataclasses and type hinting, and evaluate modern tools such as Poetry, pytest, and GitHub Actions. A new chapter introduces high-performance computing in Python, and the entire development process is enhanced with cloud-readiness in mind. You’ll follow a complete redesign and refactor of a multi-tier system from the first edition, gaining insight into how software evolves—and what it takes to do that responsibly. From system modeling and SDLC phases to data persistence, testing, and CI/CD automation, each chapter builds your engineering mindset while updating your hands-on skills. By the end of this book, you'll have mastered modern Python software engineering practices and be equipped to revise and future-proof complex systems with confidence. What you will learn Distinguish software engineering from general programming Break down and apply each phase of the SDLC to Python systems Create system models to plan architecture before writing code Apply Agile, Scrum, and other modern development methodologies Use dataclasses, pydantic, and schemas for robust data modeling Set up CI/CD pipelines with GitHub Actions and cloud build tools Write and structure unit, integration, and end-to-end tests Evaluate and integrate tools like Poetry, pytest, and Docker Who this book is for This book is for Python developers with a basic grasp of software development who want to grow into senior or staff-level engineering roles. It’s ideal for professionals looking to deepen their understanding of software architecture, system modeling, testing strategies, and cloud-aware development. Familiarity with core Python programming is required, as the book focuses on applying engineering principles to maintain, extend, and modernize real-world systems.

Learn Python coding concepts the fun way through humour, storytelling, and beginner-friendly examples that make your first step into the world of Python programming enjoyable Key Features Follow an adorable, illustrated teaching cat with sharp coding instincts as your guide Build confidence and coding skills with step-by-step explanations that gradually increase your understanding Reinforce your learning through mini-projects and exercises at the end of every chapter Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis is not your average Python programming book, because the world doesn’t need another one of those. Instead, it’s an illustrated, fun, and hands-on guide that treats learning Python like the adventure it should be. It’s designed especially for beginners who want to understand how code works without getting overwhelmed. You’ll be guided by a cheeky, know-it-all cat who’s surprisingly good at teaching Python from scratch. Don’t worry about going through it alone; a slightly moody dachshund dog is your study buddy, learning right alongside you. Each chapter introduces a core programming concept, explains it with a playful twist, and reinforces it through human-friendly examples, analogies, and exercises. Whether you’re a software professional or someone who’s never written a single line of code, this book will help you build real Python coding skills... and even enjoy the process (shocking, right?). Forget dry tutorials and walls of text. Python Illustrated speaks to visual learners, creative thinkers, cat lovers, dog lovers, and anyone who prefers their learning with a dash of humor. From writing your first function to understanding object-oriented programming, you’ll build a solid foundation in Python (without the usual headaches). What you will learn Set up Python and write your first program Understand variables, data types, and conditional statements Use loops, lists, dictionaries, and tuples to handle data efficiently Write and use functions to create reusable code Manage files and understand object-oriented programming and inheritance Debug your Python code to ensure it works flawlessly Discover how you can keep learning and building beyond this book Who this book is for This book is ideal for anyone with a sense of humour who’s new to Python or programming in general. It takes a step-by-step hands-on approach that will work for anyone with a practical mindset. Whether you're a student or a professional developer looking to expand your skill set, this book offers a comprehensive and enjoyable learning experience.

A step-by-step guide to working with programs that exploit quantum computing principles, with the help of IBM Quantum, Qiskit, and Python Key Features Understand the difference between classical computers and quantum computers Work with key quantum computational principles such as superposition, interference, and entanglement, and observe how they are leveraged on the IBM Quantum systems Run your own quantum experiments and applications by integrating with Qiskit and Python Book DescriptionThe IBM Quantum Platform was built to enable developers to learn the basics of quantum computing by providing access to high performant quantum computers and provide documentation and courses to help get up to speed with the latest features in quantum computing. Updated with new examples and changes to the platform, this edition begins with an introduction to the IBM Quantum Platform and the Quantum Information Science Kit (Qiskit) SDK. You will become well versed in the IBM Quantum Composer interface as well as running your quantum circuits on a real quantum computer. Along the way, you’ll learn some of the fundamental principles regarding quantum mechanics, qubits, quantum gates, quantum circuits, and the error mitigation techniques that are used to perform operations on qubits. As you build on your knowledge, you’ll understand the functionality of IBM Qiskit and the developer-focused resources so you can create your own quantum algorithms. You’ll learn how to monitor and optimize your quantum circuits. Lastly, you’ll look at the fundamental quantum algorithms and understand how they can be applied effectively. By the end of this quantum computing book, you'll know how to build quantum algorithms and will have gained a practical understanding of quantum computation that you can apply to your research or business. What you will learn Get familiar with the features within the IBM Quantum Platform Create and visualize quantum gates and circuits Operate quantum gates on qubits using the IBM Quantum Composer Install and run your quantum circuits on an IBM Quantum computer Discover Qiskit and its many features such as the Qiskit Runtime Get to grips with fundamental quantum algorithms and error mitigation techniques to help you get started Understand the new era of quantum utility and how this moves us closer towards quantum advantage Who this book is for This book is for Python developers who are looking to learn quantum computing from the ground up and put their knowledge to use in practical situations with the help of the IBM Quantum Platform and Qiskit. Some background in computer science and high school-level physics and math is required.

To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples. You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods. Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

Enhance your bioinformatics toolbox with practical Python recipes, tips, and tricks for key tasks like aligning sequence data, calling variants, and building Infrastructure as Code Key Features Perform sequence analysis at primary, secondary, and tertiary levels using Python libraries Solve real-world problems in the fields of phylogenetics, protein design, and annotation Use language models and other AI techniques to work with multimodal bioinformatics data Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIf you've ever felt overwhelmed by the vast number of Python tools available for bioinformatics, you're not alone. The Bioinformatics with Python Cookbook is a recipe-based guide that explores practical approaches for solving classic bioinformatics challenges, showing you which Python packages work best for each task. You’ll start with the essential Python libraries for data science and bioinformatics, then move through key workflows in sequencing analysis, quality control, alignment, and variant calling. Along the way, you’ll pick up modern coding practices, explore recent advances in bioinformatics research, and gain hands-on experience with libraries such as NumPy, pandas, and sci-kit learn. This book walks you through core bioinformatics tasks such as phylogenetic analysis and population genomics while familiarizing you with the wealth of modern public bioinformatics databases. You’ll learn cloud computing approaches used by researchers, set up workflow orchestration systems for controlling bioinformatics pipelines, and see how AI and the use of large language models (LLMs) are reshaping the field–right down to designing proteins and DNA. By the end of this book, you’ll be ready to apply Python for real bioinformatics work and launch bioinformatics pipelines for your research.What you will learn Process, analyze, and align sequencing data Call variants and interpret their biological meaning Use modern cloud infrastructure to launch bioinformatics workflows Ingest, clean, and transform data efficiently Explore how AI is shaping the future of bioinformatics Leverage imaging data for biological insights Apply single-cell sequencing to cluster and compare gene expression Who this book is for This book is for early- to mid-level practitioners in bioinformatics, data science, and software engineering who want to improve their skills and apply practical solutions to real-world problems. You should have a basic understanding of biology, including DNA, proteins, and cell structure, as well as Python programming and software engineering techniques. While prior exposure to machine learning with Python is not essential, experience with a cloud computing platform (AWS, GCP, or Azure) will be helpful.

Understand how implementing different data structures and algorithms intelligently can make your Python code and applications more maintainable and efficient Key Features • Explore functional and reactive implementations of traditional and advanced data structures • Apply a diverse range of algorithms in your Python code • Implement the skills you have learned to maximize the performance of your applications Book Description 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. What you will learn • Understand common data structures and algorithms using examples, diagrams, and exercises • Explore how more complex structures, such as priority queues and heaps, can benefit your code • Implement searching, sorting, and selection algorithms on number and string sequences • Become confident with key string-matching algorithms • Understand algorithmic paradigms and apply dynamic programming techniques • Use asymptotic notation to analyze algorithm performance with regard to time and space complexities • Write powerful, robust code using the latest features of Python Who this book is for This book is for developers and programmers who are interested in learning about data structures and algorithms in Python to write complex, flexible programs. Basic Python programming knowledge is expected.

Transform financial market data into algorithmic trading strategies and deploy them into a live trading environment with recipes leveraging modern Python libraries like pandas, Polars, and DuckDB Key Features Backtest Python trading strategies with VectorBT and Zipline Reloaded using walk-forward analysis Measure risk, performance, and alpha quality with Alphalens Reloaded and PyFolio Automate strategy execution with the Interactive Brokers API for live trading Book DescriptionGet practical Python code for algorithmic trading from Jason Strimpel, founder of PyQuant News and a veteran of global trading, risk management, and machine learning. This hands-on guide shows you how to turn market data into tested, automated trading strategies using modern Python tools. You’ll source equities, options, and futures data with OpenBB and FMP, then accelerate Python for data analysis workflows with Pandas, Polars, Parquet, DuckDB, and ArcticDB. You’ll visualize market data with Matplotlib, Seaborn, and Plotly Dash before moving into alpha research and quantitative trading techniques. Detailed recipes help you engineer alpha factors with PCA, regression, Fama-French models, SciPy, and statsmodels. You’ll design and evaluate quantitative trading strategies using VectorBT, Zipline Reloaded, Alphalens Reloaded, and PyFolio, including walk-forward analysis and risk-aware performance review. For execution, you’ll connect to the Interactive Brokers API to stream ticks, manage orders, retrieve portfolio state, and monitor live trading workflows. By the end, you’ll have reusable Python templates for researching, backtesting, evaluating, and operating algorithmic trading strategies.What you will learn Acquire equities, futures, and options data using OpenBB and FMP Process and analyze time series data efficiently with pandas and Polars Store and query massive datasets with ArcticDB, DuckDB, and Parquet Visualize trading data using Matplotlib, Seaborn, and Plotly Dash Engineer alpha factors using PCA, regression, and Fama-French models Backtest strategies with VectorBT and Zipline Reloaded frameworks Evaluate performance and risk using Alphalens Reloaded and PyFolio Deploy and automate live trades using the Interactive Brokers API Who this book is for This book is for traders, investors, and Python enthusiasts who need practical code to acquire, analyze, and automate algorithmic trading strategies using modern, high-performance Python tools. Readers should have some exposure to investing or trading, a basic familiarity with Python syntax, and a basic knowledge of libraries such as Pandas and NumPy. This book is ideal for discretionary traders who want to adopt a systematic approach and apply professional techniques, such as factor modeling, backtesting, and execution automation, to trading workflows using Python.

Future-proof your Python projects by creating flexible code that adapts to changing requirements with the help of this hands-on guide to achieving Clean Architecture Key Features Learn Clean Architecture through a series of real-world, code-centric examples and exercises Optimize system componentization, significantly reducing maintenance burden and overall complexity Apply Clean Architecture concepts confidently to new Python projects and legacy code refactoring Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly evolving tech industry, software applications struggle to keep pace with changing business needs, leaving developers grappling with complex codebases that resist change, ultimately reducing productivity and increasing technical debt. Clean Architecture with Python offers a powerful approach to address these challenges. Drawing from his extensive experience architecting cloud-native systems, Sam Keen helps you transform complex architectural challenges into digestible, implementable solutions. This book teaches essential principles for effective development, emphasizing the Pythonic implementation of Clean Architecture. Through practical examples, you'll learn how to create modular, loosely coupled systems that are easy to understand, modify, and extend. The book covers key concepts such as the Dependency Rule, separation of concerns, and domain modeling, all tailored for Python development. By the end of this book, you'll be able to apply Clean Architecture principles effectively in your Python projects. Whether you're building new systems or managing existing ones, you'll have the skills to create more maintainable and adaptable applications. This approach will enhance your ability to respond to changing requirements, setting you up for long-term success in your development career.What you will learn Apply Clean Architecture principles idiomatically in Python Implement domain-driven design to isolate core business logic Apply SOLID principles in a Pythonic context to improve code quality Structure projects for maintainability and ease of modification Develop testing techniques for cleanly architected Python applications Refactor legacy Python code to adhere to Clean Architecture principles Design scalable APIs and web applications using Clean Architecture Who this book is for If you're a Python developer struggling with maintaining and extending complex codebases, this book is for you. It's ideal for intermediate developers looking to enhance their architectural skills as well as senior developers seeking to formalize their knowledge of Clean Architecture in Python. While beginners can benefit, prior experience with Python and object-oriented programming is recommended.

Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications Book DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.What you will learn Implement robust data pipelines and manage LLM training cycles Create your own LLM and refine it with the help of hands-on examples Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring Perform supervised fine-tuning and LLM evaluation Deploy end-to-end LLM solutions using AWS and other tools Design scalable and modularLLM systems Learn about RAG applications by building a feature and inference pipeline Who this book is for This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios

Learn Django 6 by building a real-world book review site. This hands-on guide covers MVT, async views, forms, REST APIs, and testing, giving you the practical skills and confidence to build and deploy modern Python web applications.