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R 720.00
Time Series Analysis with Python Cookbook
Perform time series analysis and forecasting confidently with this Python code bank and reference manual. Access exclusive GitHub bonus chapters and hands-on recipes covering Python setup, probabilistic deep learning forecasts, frequency-domain analysis, large-scale data handling, databases, InfluxDB, and advanced visualizations.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTo use time series data to your advantage, you need to master data preparation, analysis, and forecasting. This fully refreshed second edition helps you unlock insights from time series data with new chapters on probabilistic models, signal processing techniques, and new content on transformers. You’ll work with the latest releases of popular libraries like Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet through up-to-date examples.You'll hit the ground running 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.Through detailed instructions, you'll explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR, and learn practical techniques for handling non-stationary data using power transforms, ACF and PACF plots, and decomposing time series data with seasonal patterns. The recipes then level up to cover more advanced topics such as building ML and DL models using TensorFlow and PyTorch and applying probabilistic modeling techniques. In this part, you’ll also be able to evaluate, compare, and optimize models, finishing with a strong command of wrangling data with Python.What you will learnUnderstand what makes time series data different from other dataApply imputation and interpolation strategies to handle missing dataImplement an array of models for univariate and multivariate time seriesPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsUse conformal prediction for constructing prediction intervals for time seriesWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want to learn time series analysis and forecasting techniques step by step through practical Python recipes. To get the most out of this book, you’ll need fundamental Python programming knowledge. Prior experience working with time series data to solve business problems will help
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GPU-Accelerated Computing with Python 3 and CUDA
Accelerate your Python code on the GPU using CUDA, Numba, and modern libraries to solve real-world problems faster and more efficiently.Key FeaturesBuild a solid foundation in CUDA with Python, from kernel design to execution and debuggingOptimize GPU performance with efficient memory access, CUDA streams, and multi-GPU scalingUse JAX, CuPy, RAPIDS, and Numba to accelerate numerical computing and machine learningCreate practical GPU applications, from PDE solvers to image processing and transformersBook 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 learnUnderstand GPU execution, parallelism, and the CUDA programming modelWrite, launch, and debug custom CUDA kernels in Python with CUDAProfile GPU code with NVIDIA Nsight and optimize memory accessUse CUDA streams and async execution to overlap compute and transfersApply JAX, CuPy, and RAPIDS to numerical computing and machine learningScale GPU workloads across devices using Dask and multi-GPU strategiesAccelerate PDE solvers, simulations, and image processing on the GPUBuild, train, and run a transformer model from scratch on the GPUWho this book is forPython 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.
R 1,628.00
Python for Cybersecurity
Discover an up-to-date and authoritative exploration of Python cybersecurity strategies Python For Cybersecurity: Using Python for Cyber Offense and Defense delivers an intuitive and hands-on explanation of using Python for cybersecurity. It relies on the MITRE ATT&CK framework to structure its exploration of cyberattack techniques, attack defenses, and the key cybersecurity challenges facing network administrators and other stakeholders today. Offering downloadable sample code, the book is written to help you discover how to use Python in a wide variety of cybersecurity situations, including: Reconnaissance, resource development, initial access, and executionPersistence, privilege escalation, defense evasion, and credential accessDiscovery, lateral movement, collection, and command and controlExfiltration and impact Each chapter includes discussions of several techniques and sub-techniques that could be used to achieve an attacker's objectives in any of these use cases. The ideal resource for anyone with a professional or personal interest in cybersecurity, Python For Cybersecurity offers in-depth information about a wide variety of attacks and effective, Python-based defenses against them.
R 711.00
Raspberry Pi OS System Administration with systemd and Python
The second in a new series exploring the basics of Raspberry Pi Operating System administration, this installment builds on the insights provided in Volume 1 to provide a compendium of easy- to- use and essential Raspberry Pi OS system administration for the novice user, with specific focus on Python and Python3.The overriding idea behind system administration of a modern, 21st-century Linux system such as the Raspberry Pi OS is the use of systemd to ensure that the Linux kernel works efficiently and effectively to provide these three foundation stones of computer operation and management: computer system concurrency, virtualization, and secure persistence. Exercises are included throughout to reinforce the readers’ learning goals with solutions and example code provided on the accompanying GitHub site.This book is aimed at students and practitioners looking to maximize their use of the Raspberry Pi OS. With plenty of practical examples, projects, and exercises, this volume can also be adopted in a more formal learning environment to supplement and extend the basic knowledge of a Linux operating system.
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Modelling Hydrology, Hydraulics and Contaminant Transport Systems in Python
This book covers theoretical aspects of the physical processes, derivation of the governing equations and their solutions. It focusses on hydraulics, hydrology, and contaminant transport, including implementation of computer codes with practical examples. Python-based computer codes for all the solution approaches are provided for better understanding and easy implementation. The mathematical models are demonstrated through applications and the results are analyzed through data tables, plots, and comparison with analytical and experimental data. The concepts are used to solve practical applications like surface and ground water flow, flood routing, crop water requirement and irrigation scheduling. Combines the area of computational hydraulics, hydrology, and water resources engineering with Python Gives deep description of the basic equations and the numerical solutions of both 1D and 2D problems including the numerical codes Includes step-by-step translation of numerical algorithms in computer codes with focus on learners and practitioners Demonstration of theory, mathematical models through practical applications Analysis of each example through data tables, plots, and correlation with realityThis book is aimed at senior undergraduates and graduate students in Civil Engineering, Coastal Engineering, Hydrology, and Water Resources Engineering.
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R 305.00
Introduction to R and Python for Data Analysis
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/randpythonbook/tree/master/data. Instructors can access the solutions manual via the book's website.Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students.- Does not assume any mathematical background.
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Python for Beginners
Python is an amazing programming language. It can be applied to almost any programming task. It allows for rapid development and debugging. Getting started with Python is like learning any new skill: it’s important to find a resource you connect with to guide your learning. Luckily, there’s no shortage of excellent books that can help you learn both the basic concepts of programming and the specifics of programming in Python. With the abundance of resources, it can be difficult to identify which book would be best for your situation. Python for Beginners is a concise single point of reference for all material on python.Provides concise, need-to-know information on Python types and statements, special method names, built-in functions and exceptions, commonly used standard library modules, and other prominent Python toolsOffers practical advice for each major area of development with both Python 3.x and Python 2.xBased on the latest research in cognitive science and learning theoryHelps the reader learn how to write effective, idiomatic Python code by leveraging its best—and possibly most neglected—featuresThis book focuses on enthusiastic research aspirants who work on scripting languages for automating the modules and tools, development of web applications, handling big data, complex calculations, workflow creation, rapid prototyping, and other software development purposes. It also targets graduates, postgraduates in computer science, information technology, academicians, practitioners, and research scholars.
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Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis
This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced machine learning applications, making it an invaluable resource for those eager to explore this rapidly growing field.Key Features:Throughout the book, practicality takes precedence, with hands-on coding examples accompanying each concept to facilitate an interactive learning journeyStriking a harmonious balance between theoretical foundations and practical coding, the book caters to a diverse audience, including smart home enthusiasts and researchersThe content prioritizes real-world applications, ensuring readers can immediately apply the knowledge gained to enhance smart home functionalitiesCovering Python basics, feature extraction, deep learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications
R 2,674.00
Machine Learning Guide for Oil and Gas Using Python
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
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R 791.00
Applied Engineering Mathematics with MATLAB® and Python
This comprehensive new textbook bridges the gap between mathematical theory and practical engineering applications. Organized into eight chapters, it introduces programming skills in MATLAB and Python alongside essential mathematical concepts, progressing from basic computation and linear algebra to more advanced topics such as differential equations, Fourier series, and Laplace transforms. The book develops both mathematical reasoning and computational proficiency, equipping students with essential tools for modern engineering practice. It includes numerous MATLAB and Python scripts, presented within the text and available through a dedicated GitHub repository for hands-on learning and reproducibility. Each chapter concludes with problems—many with solutions—that reinforce understanding and encourage practical application. By integrating theory with coding exercises, the book ensures that students not only grasp key concepts but also gain the skills to apply them in real-world contexts. The book is intended for undergraduate engineering students in Civil, Mechanical, Electrical, and Chemical Engineering. It is particularly suited for modules in Engineering Mathematics, Programming, Computations, and Analytical Studies, and can also serve as a supplementary reference for postgraduate students seeking applied mathematical and computational skills. Instructors will find it a valuable teaching resource that integrates theory with practice, adaptable for both semester- and year-long courses.The GitHub repository can be accesses at https://github.com/prog871/CRC-book2025.
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Productive and Efficient Data Science with Python
This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering.You’ll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You’ll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You’ll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you’ll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. What You’ll Learn Write fast and efficient code for data science and machine learningBuild robust and expressive data science pipelinesMeasure memory and CPU profile for machine learning methodsUtilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently Who This Book Is For Data scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.
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R 1,071.00
Developing Graphics Frameworks with Python and OpenGL
Developing Graphics Frameworks with Python and OpenGL shows you how to create software for rendering complete three-dimensional scenes. The authors explain the foundational theoretical concepts as well as the practical programming techniques that will enable you to create your own animated and interactive computer-generated worlds.You will learn how to combine the power of OpenGL, the most widely adopted cross-platform API for GPU programming, with the accessibility and versatility of the Python programming language. Topics you will explore include generating geometric shapes, transforming objects with matrices, applying image-based textures to surfaces, and lighting your scene. Advanced sections explain how to implement procedurally generated textures, postprocessing effects, and shadow mapping. In addition to the sophisticated graphics framework you will develop throughout this book, with the foundational knowledge you will gain, you will be able to adapt and extend the framework to achieve even more spectacular graphical results.
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R 611.00
Marketing Research With R And Python
This book is meant for readers with little or no experience in programming in R and Python, who wish to quickly learn what is necessary, and be able to conduct marketing research by running tests easily in R or Python.A number of marketing research textbooks have been using SPSS or SAS for many years. Conversely, R and Python can be downloaded and installed in a personal computer for free. Instructors and students do not have to go to a computer room in a university to use SPSS or SAS anymore. Instead, students can run R or Python on their personal computers.For any company, growth comes either from organic growth of existing products, or from launching successful new products. Due to competition in the marketplace, each company's marketer must determine whether or not it is time to develop and launch a new product:This book covers important frameworks and concepts of marketing research for developing a new product.
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