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Python Arithmetic
The book is a gentle introduction to Python using arithmetic, and vice versa, with a historical perspective encompassing programming languages within the wider process of development of mathematical notation. The revisitation of typical algorithms that are the core of elementary mathematical knowledge helps to grasp their essence and to clarify some assumptions that are often taken for granted but are very profound and of a very general nature.The first mathematician to define a systematic system for generating numbers was Archimedes of Syracuse in the third century B.C. The Archimedean system, which was defined in a book with the Latin title Arenarius, was not intended to define all numbers, but only very large numbers [13, 22, 23]. However, it can be considered the first system with the three main characteristics of a counting system that have the most important properties for complete arithmetic adequacy: creativity, infinity, and recursion. Creativity means that each numeral is new for numerals that precede it; infinity means that after any numeral there is always another numeral; recursion means that after an initial sequence of numerals coinciding with the digits of the system, digits repeat regularly in all subsequent numerals. Since the numerals are finite expressions of digits, their lengths increase along their generation. In the next chapter, Python is briefly introduced by linking this language to standard mathematical notation, which took its current form throughout a long process that extends from the introduction of decimal numerals to the eighteenth century, particularly within Euler’s notational and conceptual framework. The third chapter is devoted to counting algorithms, showing that something that is usually taken for granted has intriguing aspects that deserve a very subtle analysis: the authors will show that the Python representation of counting algorithms is very informative and demonstrates the informational nature of numbers.
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Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
R 10,134.08
Python for Mathematics
Python for Mathematics introduces readers to effective methods for doing mathematics using the Python programming language. Most programming texts introduce readers to the building blocks of programming and build up to using more sophisticated tools for a specific purpose, like doing particular mathematical tasks. This is akin to teaching someone how to forge metal so as to make a nail, and then slowly working up to using sophisticated power tools so as to actually build something. This book does things in a different way, by first getting readers to begin using and understanding the tools that are going to be helpful to them as mathematicians, and only then moving onto the granular details. In this way, the practical application of the tools can aid in the understanding of the theory.Features· Complete with engaging, practical exercises· Many useful and detailed coding examples· Suitable for undergraduates in mathematics, and other quantitative sciences· Empowers readers to design and create their own Python tools.
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Symbolic Mathematics with Python
This book provides a hands-on approach to computer symbolic computation using the elementary commands of Python. Symbolic computer mathematics is the study of algorithms that can be implemented as computer programs which provide exact results rather than numerical approximations. The author begins by discussing Python essentials an number theory. Then, the book covers the simplification and evaluation of expressions involving multivariate rational functions, the exact solutions of systems of linear equations, and applications of polynomial algebra. Programs in symbolic differentiation and indefinite integration programs are also developed.
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Mathematical Methods using Python
This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc.An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions. The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready to run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses.Key Features:A unique teaching approach which merges mathematical methods and the Python programming skills which physicists and engineering students need in their coursesUses examples and models from physical and engineering systems, to motivate the mathematics being taughtStudents learn to solve scientific problems in three different ways: traditional pen-and-paper methods, using scientific numerical techniques with NumPy and SciPy, and using Symbolic Python (SymPy).
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Mathematical Methods using Python
This advanced undergraduate textbook presents a new approach to teaching mathematical methods for scientists and engineers. It provides a practical, pedagogical introduction to utilizing Python in Mathematical and Computational Methods courses. Both analytical and computational examples are integrated from its start. Each chapter concludes with a set of problems designed to help students hone their skills in mathematical techniques, computer programming, and numerical analysis. The book places less emphasis on mathematical proofs, and more emphasis on how to use computers for both symbolic and numerical calculations. It contains 182 extensively documented coding examples, based on topics that students will encounter in their advanced courses in Mechanics, Electronics, Optics, Electromagnetism, Quantum Mechanics etc.An introductory chapter gives students a crash course in Python programming and the most often used libraries (SymPy, NumPy, SciPy, Matplotlib). This is followed by chapters dedicated to differentiation, integration, vectors and multiple integration techniques. The next group of chapters covers complex numbers, matrices, vector analysis and vector spaces. Extensive chapters cover ordinary and partial differential equations, followed by chapters on nonlinear systems and on the analysis of experimental data using linear and nonlinear regression techniques, Fourier transforms, binomial and Gaussian distributions. The book is accompanied by a dedicated GitHub website, which contains all codes from the book in the form of ready to-run Jupyter notebooks. A detailed solutions manual is also available for instructors using the textbook in their courses.Key Features:· A unique teaching approach which merges mathematical methods and the Python programming skills which physicists and engineering students need in their courses.· Uses examples and models from physical and engineering systems, to motivate the mathematics being taught.· Students learn to solve scientific problems in three different ways: traditional pen-and-paper methods, using scientific numerical techniques with NumPy and SciPy, and using Symbolic Python (SymPy).Vasilis Pagonis is Professor of Physics Emeritus at McDaniel College, Maryland, USA. His research area is applications of thermally and optically stimulated luminescence. He taught courses in mathematical physics, classical and quantum mechanics, analog and digital electronics and numerous general science courses. Dr. Pagonis’ resume lists more than 200 peer-reviewed publications in international journals. He is currently associate editor of the journal Radiation Measurements. He is co-author with Christopher Kulp of the undergraduate textbook “Classical Mechanics: a computational approach, with examples in Python and Mathematica” (CRC Press, 2020). He has also co-authored four graduate-level textbooks in the field of luminescence dosimetry, and most recently published the book “Luminescence Signal analysis using Python” (S
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Mathematics and Python Programming
This book contains 120 Python programs and more than 110 illustrations useful both to students of science of the first university courses, as well as high school students and teachers. All the programs and graphics of the book have been developed using free software, which you can obtain free and legally on the internet, so it is not necessary to buy any software. Python is currently the most popular language for teaching introductory computer science courses at top-ranked U.S. universities. You can freely write Python programs whether you are a Windows user or whether you prefer Mac or Linux. A free pdf excerpt is available at our website www.pysamples.com. The buyers of the book can also download the code of the Python programs. The book consists of a detailed introduction to Python, followed by ten chapters of mathematics with its corresponding Python programs, results and graphs.
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Explorations In Numerical Analysis: Python Edition
This textbook is intended to introduce advanced undergraduate and early-career graduate students to the field of numerical analysis. This field pertains to the design, analysis, and implementation of algorithms for the approximate solution of mathematical problems that arise in applications spanning science and engineering, and are not practical to solve using analytical techniques such as those taught in courses in calculus, linear algebra or differential equations.Topics covered include computer arithmetic, error analysis, solution of systems of linear equations, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, and partial differential equations. For each problem considered, the presentation includes the derivation of solution techniques, analysis of their efficiency, accuracy and robustness, and details of their implementation, illustrated through the Python programming language.This text is suitable for a year-long sequence in numerical analysis, and can also be used for a one-semester course in numerical linear algebra.
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Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
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Mathematical Logic through Python
Using a unique pedagogical approach, this text introduces mathematical logic by guiding students in implementing the underlying logical concepts and mathematical proofs via Python programming. This approach, tailored to the unique intuitions and strengths of the ever-growing population of programming-savvy students, brings mathematical logic into the comfort zone of these students and provides clarity that can only be achieved by a deep hands-on understanding and the satisfaction of having created working code. While the approach is unique, the text follows the same set of topics typically covered in a one-semester undergraduate course, including propositional logic and first-order predicate logic, culminating in a proof of Gödel's completeness theorem. A sneak peek to Gödel's incompleteness theorem is also provided. The textbook is accompanied by an extensive collection of programming tasks, code skeletons, and unit tests. Familiarity with proofs and basic proficiency in Python is assumed.
R 1,311.25
Numerical Python
Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
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Numbers And Functions: Theory, Formulation And Python Codes
This unique volume covers two fundamental elements of computational methods — numbers and functions. It provides an in-depth discussion of the behaviors of numbers, including both real and complex numbers. The discussion leads to the important closure properties of numbers, ensuring solution consistence and existence, and also possible failure in numerical computations in science and engineering.This text then introduces types of functions that take numbers as independent variables and produce a single number. Approaches for constructing inverse functions are also provided. Frequently used basis functions are introduced, with detailed studies on their properties and behaviors. Techniques are presented for constructing basis functions and their use in function approximation in computational methods.
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Calculus: Formulations And Solutions With Python
This comprehensive volume explores differentiation and integration, detailing their theories, concepts, and formulations. The book introduces various techniques for computing these mathematical elements for different types of functions and presents their applications.Python code is extensively used throughout the book, allowing readers to practice and interact with the concepts in real-time. This hands-on approach helps in comprehending the theory, techniques, and results of computational operations in differentiation and integration. Real-world engineering problems are connected to the theoretical discussions through numerous examples.Written in Jupyter notebook format, the useful reference text offers a unified environment for theory description, code execution, and real-time interaction, making it ideal for reading, practicing, and further exploration.
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Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
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Python Algorithms
Python Algorithms explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself.
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Python Algorithms
Python Algorithms, Second Edition explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science in a highly readable manner. It covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others.
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Absolute Beginner's Guide to Python Programming
Written as an illustrated, step-by-step guide, this book will introduce you to Python with examples using the latest version of the language. You'll begin by learning to set up your Python environment. The next few chapters cover the basics of Python such as language classifications, Python language syntax, and how to write a program. Next, you will learn how to work with variables, basic data types, arithmetic, companion, and Boolean operators, followed by lab exercises. Further, the book covers flow control, using functions, and exception handling, as well as the principles of object-oriented programming and building an interface design. The last section explains how to develop a game by installing PyGame and how to use basic animation, and concludes with coverage of Python web development with web servers and Python web frameworks. The Absolute Beginners Guide to Python Programming will give you the tools, confidence, and inspiration to start writing Python programs. If you are a programmer, developer, or a student, or someone who wants to learn on their own, this book is for you.What You Will LearnGain an understanding of computer programming Understand different data and data typesWork with Classes and OOPBuild interfaces, simple games, and web development with PythonWho This Book Is ForSoftware programmers, developers or students, or anyone who wants to learn Python programming on their own.
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Python for Engineering and Scientific Computing
It's finally here-your guide to Python for engineers and scientists, by an engineer and scientist! Get to know your development environments and the key Python modules you'll need: NumPy, SymPy, SciPy, Matplotlib, and VPython. Understand basic Python program structures and walk through practical exercises that start simple and increase in complexity as you work your way through the book. With information on statistical calculations, Boolean algebra, and interactive programming with Tkinter, this Python guide belongs on every scientist's shelf!Highlights include:1) Program structures2) NumPy3) Matplotlib4) SymPy5) SciPy6) VPython7) Tkinter8) Numerical calculations9) Statistical calculations10) Boolean algebra
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Practical Computer Architecture with Python and ARM
Learn computer architecture with Python and ARM, simulating assembly program execution and designing a computer simulatorPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesBuild a computer simulator with Python: Learn computer architecture by designing and constructing a simulatorPython for architecture: Use Python to simulate and execute assembly language instructionsARM programming on Raspberry Pi: Explore ARM assembly language and run programs on Raspberry PiBook DescriptionThis comprehensive guide offers a unique and immersive learning experience by combining Python programming with ARM architecture.Starting with an introduction to computer architecture and the flow of data within a computer system, you’ll progress to building your own interpreter using Python. You’ll see how this foundation enables the simulation of computer operations and learn ways to enhance a simulator by adding new instructions and displaying improved results.As you advance, you’ll explore the TC1 Assembler and Simulator Program to gain insights into instruction analysis and explore practical examples of simulators. This will help you build essential skills in understanding complex computer instructions, strengthening your grasp of computer architecture. Moreover, you’ll be introduced to the Raspberry Pi operating system, preparing you to delve into the detailed language of the ARM computer. This includes exploring the ARM instruction set architecture, data-processing instructions, subroutines, and the stack.With clear explanations, practical examples, and coding exercises, this resource will enable you to design and construct your own computer simulator, simulate assembly language programs, and leverage the Raspberry Pi for ARM programming.What you will learnMaster the core principles of computer architectureUnderstand the role of registers, memory, and data flow in computersDiscover how to design and implement a computer simulator using PythonSimulate and execute assembly language programs on the simulatorEnhance the simulator using new instructions for improved outputAnalyze complex computer instructions for deeper architectural understandingExplore the ARM instruction set and data processing on the Raspberry PiDevelop proficiency in writing, assembling, and running ARM code on the Raspberry PiWho this book is forThis book is for university students studying computer science, particularly those enrolled in a computer architecture module. With its practical approach and succinct explanations, it is also suitable for hobbyists, enthusiasts, and self-learners seeking a deeper understanding of computer systems. The book assumes foundational knowledge of number bases, binary arithmetic, and Boolean logic concepts. While it primarily caters to the computer science field, this book is less geared toward electrical or electronics engineering.
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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Pythonic Programming
Make your good Python code even better by following proven and effective pythonic programming tips. Avoid logical errors that usually go undetected by Python linters and code formatters, such as frequent data look-ups in long lists, improper use of local and global variables, and mishandled user input. Discover rare language features, like rational numbers, set comprehensions, counters, and pickling, that may boost your productivity. Discover how to apply general programming patterns, including caching, in your Python code. Become a better-than-average Python programmer, and develop self-documented, maintainable, easy-to-understand programs that are fast to run and hard to break.Python is one of the most popular and rapidly growing modern programming languages. With more than 200 standard libraries and even more third-party libraries, it reaches into the software development areas as diverse as artificial intelligence, bioinformatics, natural language processing, and computer vision. Find out how to improve your understanding of the spirit of the language by using one hundred pythonic tips to make your code safer, faster, and better documented.This programming style manual is a quick reference of helpful hints and a random source of inspiration. Choose the suitable data structures for searching and sorting jobs and become aware of how a wrong choice may cause your application to be completely ineffective. Understand global and local variables, class and instance attributes, and information-hiding techniques. Create functions with flexible interfaces. Manage intermediate computation results by caching them in files and memory to improve performance and reliability. Polish your documentation skills to make your code easy for other programmers to understand. As a bonus, discover Easter eggs cleverly planted in the standard library by its developers.Polish, secure, and speed-up your Python applications, and make them easier to maintain by following pythonic programming tips.What You Need:You will need a Python interpreter (ideally, version 3.4 or above) and the standard Python library that usually comes with the interpreter.
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Numerical Methods with Python
Introduces students to appropriate use of computer programming within the scientific disciplines using Python. Discusses several common applications of programming and implementation using real world examples and hands on programming exercises. Students learn how to model situations such as image recognition, medical diagnosis, spread of disease, and others. The text could be used by students and lecturers for courses in Python, Numerical Methods, or as a first course in Data Science.
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Learning Python
Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated sixth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to programming or a professional developer versed in other languages.Complete with quizzes, exercises, and helpful illustrations, this easy-to-follow self-paced tutorial gets you started with Python 3.12 and all other releases in use today. With a pragmatic focus on what you need to know, it also introduces some advanced language features that have become increasingly common in Python code.This book helps you:Explore Python's built-in object types such as strings, lists, dictionaries, and filesCreate and process objects with Python statements, and learn Python's syntax modelUse functions and functional programming to avoid redundancy and maximize reuseOrganize code into larger components with modules and packagesCode robust programs with Python's exception handling and development toolsApply object-oriented programming and classes to make code customizableSurvey advanced Python tools including decorators, descriptors, and metaclassesWrite idiomatic Python code that runs portably across a wide variety of platforms
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Python Programming for Mathematics
Python Programming for Mathematics focuses on the practical use of the Python language in a range of different areas of mathematics. Through fifty-five exercises of increasing difficulty, the book provides an expansive overview of the power of using programming to solve complex mathematical problems.This book is intended for undergraduate and graduate students who already have learned the basics of Python programming and would like to learn how to apply that programming skill in mathematics.FeaturesInnovative style that teaches programming skills via mathematical exercises.Ideal as a main textbook for Python for Mathematics courses, or as a supplementary resource for Numerical Analysis and Scientific Computing courses.
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