Search results for "python arithmetic"
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CPython
Deep Dive CPython explores the internal mechanics of CPython, the widely used Python interpreter written in C. Starting with a practical guide on downloading and compiling the CPython source, this book is perfect for developers eager to understand Python's behaviour at a fundamental level. The book takes readers from basic concepts to complex details with a systematic breakdown of core components. It covers everything from CPython's data structures like PyObject and PyTypeObject to object lifecycle management, giving insight into memory allocation and object reference counting. Each chapter illustrates CPython's architecture, such as Python's "everything is an object" philosophy, list handling, string manipulation, and dictionary operations. Readers will explore Python's REPL modifications, string internals, and custom type creation with practical examples, like crafting a "backflipping" PyKittyType. Detailed sections on Python's virtual machine operations, bytecode generation, and exception handling enrich readers' understanding of how Python code is parsed, compiled, and executed. This book is a thorough guide for readers who want to go beyond basic Python use and understand how it works internally. Covering complex concepts like generators, iterators, descriptors, and metaclasses, this book equips readers with a thorough grasp of Python's performance optimization and design complexities. What you will learn: How to download, compile, and modify CPython's source codeGain insight into fundamental structures like PyObject and PyTypeObject, Understand Python's detailed handling of lists, strings, dictionaries, and the REPL environment.What are bytecode generation, custom types, and the inner workings of Python's virtual machine. Who this book is for: Python programmers aiming to gain a deeper understanding of Python's internals and move beyond standard usage, as well as software professionals interested in CPython's C-based implementation and core architecture.
R 1,368.00
Elementary Mechanics Using Python
This book – specifically developed as a novel textbook on elementary classical mechanics – shows how analytical and numerical methods can be seamlessly integrated to solve physics problems. This approach allows students to solve more advanced and applied problems at an earlier stage and equips them to deal with real-world examples well beyond the typical special cases treated in standard textbooks.Another advantage of this approach is that students are brought closer to the way physics is actually discovered and applied, as they are introduced right from the start to a more exploratory way of understanding phenomena and of developing their physical concepts.While not a requirement, it is advantageous for the reader to have some prior knowledge of scientific programming with a scripting-type language. This edition of the book uses Python, and a chapter devoted to the basics of scientific programming with Python is included. A parallel edition using Matlab instead of Python is also available.Last but not least, each chapter is accompanied by an extensive set of course-tested exercises and solutions.
R 2,198.00
R 3,597.70
Programming with Python for Engineers
This book introduces computing and programming with undergraduate engineering students in mind. It uses Python (Version 3) as the programming language, chosen for its simplicity, readability, wide applicability and large collection of libraries. After introducing engineering-related Python libraries, such as NumPy, Pandas, Matplotlib, Sci-kit, Programming with Python for Engineers shows how Python can be used to implement methods common in a wide spectrum of engineering-related problems drawn from (for example): design, control, decision-making, scheduling and planning.Important features of the book include the following:· The book contains interactive content for illustration of important concepts, where the user can provide input and by clicking buttons, trace through the steps.Each chapter is also accessible as a Jupyter Notebook page and every code piece is executable. This allows the readers to run code examples in chapters immediately, to make changes and gain a better grasp of the concepts presented.· The coverage of topics is complemented by illustrative examples and exercises.For instructors adopting the textbook, a solutions manual is provided.
R 3,297.00
AI-Assisted Python for Nonprogrammers
It's hard to exaggerate Python's popularity: It's used for everything from data science to servers to the web. It's also famously "easy" to learn. However, learning to program in Python can still be a frustrating experience. AI-Assisted Python for Nonprogrammers is designed to give you the context you need to build a deep understanding not just of Python's syntax but also of the concepts behind it, even if you don't have any previous programming experience.Through clear explanations and explorations, author Reuven Lerner will have you writing simple Python programs in no time. By collaborating with AI chatbots to complete focused hands-on exercises, you'll reinforce core concepts like variables and values, conditions and loops, functions, and modules (while also getting a taste of coding with GenAI). But most importantly, you'll learn how to think about computers and software like a programmer—setting you up to better participate in technical conversations at your job and understand the influence of technology on our world.Write simple Python programs using core data structures, conditions, and loopsWrite and use Python functionsUse Python modulesGain the confidence to continue your Python journey
R 1,484.00
R 562.00
R 1,908.00
R 1,088.59
Computational Problems for Physics
Our future scientists and professionals must be conversant in computational techniques. In order to facilitate integration of computer methods into existing physics courses, Computational Problems for Physics offers a large number of worked examples and problems with fully guided solutions in Python (as well as Mathematica, Java, C, Fortran, and Maple on the Web). The book can be used as a self-study guide for learning how to use computer methods in physics. Fully revised, this second edition includes:A chapter on neural networks, machine learning, and artificial intelligence, with the building of simple networks, and the use of machine learning software.A chapter on quantum computing with some problems to be run on the IBM Quantum Computer.A chapter on general relativity with manipulations of the field equations and with computation of GR corrections to classical mechanics.An expanded coverage of principal component analysis.A crucial resource for students beginning their study of computational physics with in-depth and engaging problems and exercises.
R 8,572.00
R 6,328.34
Pythonic Geodynamics
This book addresses students and young researchers who want to learn to use numerical modeling to solve problems in geodynamics. Intended as an easy-to-use and self-learning guide, readers only need a basic background in calculus to approach most of the material. The book difficulty increases very gradually, through four distinct parts. The first is an introduction to the Python techniques necessary to visualize and run vectorial calculations. The second is an overview with several examples on classical Mechanics with examples taken from standard introductory physics books. The third part is a detailed description of how to write Lagrangian, Eulerian and Particles in Cell codes for solving linear and non-linear continuum mechanics problems. Finally the last one address advanced techniques like tree-codes, Boundary Elements, and illustrates several applications to Geodynamics. The entire book is organized around numerous examples in Python, aiming at encouraging the reader to learn by experimenting and experiencing, not by theory.
R 5,924.75
R 763.00
R 520.00
Applied Mathematics with Open-Source Software
Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic.Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.
R 7,579.51
R 1,327.96
R 458.00
R 951.14
Python for Programmers
This book is intended primarily for developers who have little or no experience with Python or Pandas. It contains a fast-paced introduction to Python and Python-based solutions to various tasks. Chapter 1 provides a quick tour of basic Python 3, followed by a chapter that shows how to work with loops and conditional logic in Python. Chapter 3 discusses data structures in Python, followed by a chapter that features code samples for tasks with strings and arrays in Python. Chapter 5 contains concepts in object-oriented programming, along with code samples that illustrate how they are implemented in Python. Chapter 6 introduces recursion and some fundamental topics in combinatorics. Finally, the appendix provides an introduction to Pandas. Companion files with code and figures are available for downloading from the publisher.Features:Provides the reader with basic Python 3 and Pandas programming concepts.Companion files with code and figures
R 1,277.00
R 611.00
R 1,092.75
R 1,215.48
High Performance Python
Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsProcess DataFrames quickly with Pandas, Dask, and PolarsSpeed up your neural networks and GPU computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clusters
R 1,505.00
R 1,498.00
Arithmetics
Number theory is a branch of mathematics which draws its vitality from a rich historical background. It is also traditionally nourished through interactions with other areas of research, such as algebra, algebraic geometry, topology, complex analysis and harmonic analysis. More recently, it has made a spectacular appearance in the field of theoretical computer science and in questions of communication, cryptography and error-correcting codes. Providing an elementary introduction to the central topics in number theory, this book spans multiple areas of research. The first part corresponds to an advanced undergraduate course. All of the statements given in this part are of course accompanied by their proofs, with perhaps the exception of some results appearing at the end of the chapters. A copious list of exercises, of varying difficulty, are also included here. The second part is of a higher level and is relevant for the first year of graduate school. It contains an introduction to elliptic curves and a chapter entitled “Developments and Open Problems”, which introduces and brings together various themes oriented toward ongoing mathematical research. Given the multifaceted nature of number theory, the primary aims of this book are to: - provide an overview of the various forms of mathematics useful for studying numbers - demonstrate the necessity of deep and classical themes such as Gauss sums - highlight the role that arithmetic plays in modern applied mathematics - include recent proofs such as the polynomial primality algorithm - approach subjects of contemporary research such as elliptic curves - illustrate the beauty of arithmetic The prerequisites for this text are undergraduate level algebra and a little topology of Rn. It will be of use to undergraduates, graduates and phd students, and may also appeal to professional mathematicians as a reference text.
R 2,505.72
Python Distilled
Expert insight for modern Python (3.6+) coding from the author of Python Essential Reference Python educator Dave Beazley’s concise handbook focuses on the essential core of the Python programming language, with code examples to illuminate how Python works and how to structure programs that can be more easily explained, tested, and debugged. Rather than trying to cover every possible feature and quirk of a 30-year-old language, this pragmatic guide provides a concise narrative related to fundamental programming topics that form the foundation for Python projects of any size including Data abstractionControl flowProgram structureFunctions: master functions and functional programming idiomsObjects: control objects and master the "protocols" that define their behaviorModules: plan for project growth by understanding modules and packagesGeneratorsClasses: understand classes from both high-level and technical perspectivesI/O handling: proper techniques and abstractionsDicts "This is a pragmatic book that presents some of the most important topics about the Python programming language in a concise form, designed to make it easier to find out the most relevant information bits in a context where resources abound and frequently are just too overwhelming." —Victor Domingos, Software Developer, Slim Business Solutions, The No Title® Tech Blog
R 1,628.00
R 1,160.00
Next Generation Arithmetic
This book constitutes the refereed proceedings of the Third International Conference on Next Generation Arithmetic, CoNGA 2022, which was held in Singapore, during March 1–3, 2022. The 8 full papers included in this book were carefully reviewed and selected from 12 submissions. They deal with emerging technologies for computer arithmetic focusing on the demands of both AI and high-performance computing.
R 2,530.11
R 618.00
Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
R 6,893.02
R 781.77
R 1,126.37
Elementary Mechanics Using Python
This book – specifically developed as a novel textbook on elementary classical mechanics – shows how analytical and numerical methods can be seamlessly integrated to solve physics problems. This approach allows students to solve more advanced and applied problems at an earlier stage and equips them to deal with real-world examples well beyond the typical special cases treated in standard textbooks.Another advantage of this approach is that students are brought closer to the way physics is actually discovered and applied, as they are introduced right from the start to a more exploratory way of understanding phenomena and of developing their physical concepts.While not a requirement, it is advantageous for the reader to have some prior knowledge of scientific programming with a scripting-type language. This edition of the book uses Python, and a chapter devoted to the basics of scientific programming with Python is included. A parallel edition using Matlab instead of Python is also available.Last but not least, each chapter is accompanied by an extensive set of course-tested exercises and solutions.
R 2,198.00
R 397.00
R 618.00
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