Search results for "Handbook of Computer Programming with Python"
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Applying Math with Python
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific librariesKey FeaturesCompute complex mathematical problems using programming logic with the help of step-by-step recipesLearn how to utilize Python's libraries for computation, mathematical modeling, and statisticsDiscover simple yet effective techniques for solving mathematical equations and apply them in real-world statisticsBook DescriptionPython, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.What you will learnGet familiar with basic packages, tools, and libraries in Python for solving mathematical problemsExplore various techniques that will help you to solve computational mathematical problemsUnderstand the core concepts of applied mathematics and how you can apply them in computer scienceDiscover how to choose the most suitable package, tool, or technique to solve a certain problemImplement basic mathematical plotting, change plot styles, and add labels to the plots using MatplotlibGet to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methodsWho this book is forThis book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
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Computer Programming in C for Beginners
This textbook is an ideal introduction in college courses or self-study for learning computer programming using the C language. Written for those with minimal or no programming experience, Computer Programming in C for Beginners offers a heavily guided, hands-on approach that enables the reader to quickly start programming, and then progresses to cover the major concepts of C programming that are critical for an early stage programmer to know and understand. While the progression of topics is conventional, their treatment is innovative and designed for rapid understanding of the many concepts in C that have traditionally proven difficult for beginners, such as variable typing and scope, function definition, passing by value, pointers, passing by reference, arrays, structures, basic memory management, dynamic memory allocation, and linked lists, as well as an introductory treatment of searching and sorting algorithms. Written in an informal but clear narrative, the book uses extensive examples throughout and provides detailed guidance on how to write the C code to achieve the objectives of the example problems. Derived from the author’s many years of teaching hands-on college courses, it encourages the reader to follow along by programming the progressively more complex exercise programs presented. In some sections, errors are purposely inserted into the code to teach the reader about the common pitfalls of programming in general, and the C language in particular.
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Eye-Tracking with Python and Pylink
Several Python programming books feature tools designed for experimental psychologists. What sets this book apart is its focus on eye-tracking.Eye-tracking is a widely used research technique in psychology and neuroscience labs. Research grade eye-trackers are typically faster, more accurate, and of course, more expensive than the ones seen in consumer goods or usability labs. Not surprisingly, a successful eye-tracking study usually requires sophisticated computer programming. Easy syntax and flexibility make Python a perfect choice for this task, especially for psychology researchers with little or no computer programming experience.This book offers detailed coverage of the Pylink library, a Python interface for the gold standard EyeLink ® eye-trackers, with many step-by-step example scripts. This book is a useful reference for eye-tracking researchers, but you can also use it as a textbook for graduate-level programming courses.
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Foundations for Analytics with Python
If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments
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Hands-On Simulation Modeling with Python
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with easeKey FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook DescriptionSimulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is forThis book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.
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Programming for Computations - MATLAB/Octave
This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.
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Getting Started with Python Data Analysis
Learn to use powerful Python libraries for effective data processing and analysisAbout This Book• Learn the basic processing steps in data analysis and how to use Python in this area through supported packages, especially Numpy, Pandas, and Matplotlib• Create, manipulate, and analyze your data to extract useful information to optimize your system• A hands-on guide to help you learn data analysis using PythonWho This Book Is ForIf you are a Python developer who wants to get started with data analysis and you need a quick introductory guide to the python data analysis libraries, then this book is for you.What You Will Learn• Understand the importance of data analysis and get familiar with its processing steps• Get acquainted with Numpy to use with arrays and array-oriented computing in data analysis• Create effective visualizations to present your data using Matplotlib• Process and analyze data using the time series capabilities of Pandas• Interact with different kind of database systems, such as file, disk format, Mongo, and Redis• Apply the supported Python package to data analysis applications through examples• Explore predictive analytics and machine learning algorithms using Scikit-learn, a Python libraryIn DetailData analysis is the process of applying logical and analytical reasoning to study each component of data. Python is a multi-domain, high-level, programming language. It's often used as a scripting language because of its forgiving syntax and operability with a wide variety of different eco-systems. Python has powerful standard libraries or toolkits such as Pylearn2 and Hebel, which offers a fast, reliable, cross-platform environment for data analysis.With this book, we will get you started with Python data analysis and show you what its advantages are.The book starts by introducing the principles of data analysis and supported libraries, along with NumPy basics for statistic and data processing. Next it provides an overview of the Pandas package and uses its powerful features to solve data processing problems.Moving on, the book takes you through a brief overview of the Matplotlib API and some common plotting functions for DataFrame such as plot. Next, it will teach you to manipulate the time and data structure, and load and store data in a file or database using Python packages. The book will also teach you how to apply powerful packages in Python to process raw data into pure and helpful data using examples.Finally, the book gives you a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or build helpful products, such as recommendations and predictions using scikit-learn. Style and approachThis is an easy-to-follow, step-by-step guide to get you familiar with data analysis and the libraries supported by Python. Topics are explained with real-world examples wherever required.
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Learning Geospatial Analysis with Python
Harness the powerful Python programming language to navigate the realms of geographic information systems, remote sensing, topography, and more, while embracing a guiding framework for effective geospatial analysisKey FeaturesCreate GIS solutions using the new features introduced in Python 3.10Explore a range of GIS tools and libraries, including PostGIS, QGIS, and PROJIdentify the tools and resources that best align with your specific needsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.What you will learnAutomate geospatial analysis workflows using PythonUnderstand the different formats in which geospatial data is availableUnleash geospatial tech tools to create stunning visualizationsCreate thematic maps with Python tools such as PyShp, OGR, and the Python Imaging LibraryBuild a geospatial Python toolbox for analysis and application developmentUnlock remote sensing secrets, detect changes, and process imageryLeverage ChatGPT for solving Python geospatial solutionsApply geospatial analysis to real-time data tracking and storm chasingWho this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
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official Raspberry Pi Camera Module guide, 2nd Edition
Add the power of HDR photography, Full HD video, and AI image recognition to your Raspberry Pi projects with Camera Modules.Give intelligence and depth to your creations, making your projects smarter and more responsive. Plus, the new AI Kit supercharges your Raspberry Pi Camera Module with image recognition, enabling it to act and react to the world around it.This book is your step-by-step guide to adding a Camera Module to your Raspberry Pi board. You'll master still images, capture video clips as digital files, and control the camera with shell commands and Python code.Discover the numerous modes and effects available, and use the camera in a variety of exciting projects across 18 packed chapters:Getting started.Capturing photos and videos.Control the camera with precision.Add artificial intelligence with the Al Kit.Time-lapse photography.Selfies and stop-motion video.Build a bird box camera.Live-stream video and stills....and much more!
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Learn Web Development with Python
A comprehensive guide to Python programming for web development using the most popular Python web framework - DjangoKey FeaturesLearn the fundamentals of programming with Python and building web appsBuild web applications from scratch with DjangoCreate real-world RESTful web services with the latest Django frameworkBook DescriptionIf you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating real-world applications.By the end of this Learning Path, you will have a full understanding of how Django works and how to use it to build web applications from scratch.This Learning Path includes content from the following Packt products:Learn Python Programming by Fabrizio RomanoDjango RESTful Web Services by Gastón C. HillarDjango Design Patterns and Best Practices by Arun RavindranWhat you will learnExplore the fundamentals of Python programming with interactive projectsGrasp essential coding concepts along with the basics of data structures and control flowDevelop RESTful APIs from scratch with Django and the Django REST FrameworkCreate automated tests for RESTful web servicesDebug, test, and profile RESTful web services with Django and the Django REST FrameworkUse Django with other technologies such as Redis and CeleryWho this book is forIf you have little experience in coding or Python and want to learn how to build full-fledged web apps, this Learning Path is for you. No prior experience with RESTful web services, Python, or Django is required, but basic Python programming experience is needed to understand the concepts covered.
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Official Raspberry Pi Handbook 2026
Dive into the world of Raspberry Pi with this huge book of tutorials, project showcases, guides, product reviews, and much more from the pages of Raspberry Pi Official Magazine.Raspberry Pi 500 and Raspberry Pi 500+ are here, taking Raspberry Pi to new heights. It’s the power of Raspberry Pi 5 in a full desktop experience and we have all the specs and details. That's not all - we have plenty of Raspberry Pi Pico 2, Raspberry Pi Zero 2, Raspberry Pi 5, and Raspberry Pi 4/400 content squeezed into the book. With 200 pages packed full of maker goodness, you'll find inspiration no matter which Raspberry Pi model you have.There's something for everyone in here:A get started guide covering every Raspberry PiEverything you need to know about the new Raspberry Pi 500+Inspiring projects to give you your next project ideaLearn how to build a Raspberry Pi 5-powered media playerExplore the world around you with roving robotsPlay retro horror games on Raspberry Pi 5This bumper book is your definitive guide to everything Raspberry Pi. It's essential for any maker with big dreams and a thirst for knowledge.
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Twisted Network Programming Essentials, 2e
Get started with Twisted, the event-driven networking framework written in Python. With this introductory guide, you'll learn the key concepts and design patterns to build event-driven client and server applications for many popular networking protocols. You'll also learn the tools to build new protocols using Twisted's primitives. Start by building basic TCP clients and servers, and then focus on deploying production-grade applications with the Twisted Application infrastructure. Along the way, you can play with and extend examples of common tasks you'll face when building network applications. If you're familiar with Python, you're ready for Twisted. Learn the core components of Twisted servers and clients Write asynchronous code with the Deferred API Construct HTTP servers with Twisted's high-level web APIs Use the Agent API to develop flexible web clients Configure and deploy Twisted services in a robust and standardized fashion Access databases using Twisted's nonblocking interface Add common server components: logging, authentication, threads and processes, and testing Explore ways to build clients and servers for IRC, popular mail protocols, and SSH
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Mathematical and Computer Programming Techniques for Computer Graphics
Provides a comprehensive and detailed coverage of the fundamentals of programming techniques for computer graphicsUses lots of code examples, encouraging the reader to explore and experiment with data and computer programs (in the C programming language)
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Electrodynamics Tutorials with Python Simulations
This book provides an accessible introduction to intermediate-level electrodynamics with computational approaches to complement a traditional mathematical treatment of the subject. It covers key topics in electrodynamics, such as electromagnetic fields, forces, potentials, and waves as well as Special Theory of Relativity.Through intuition-building examples and visualizations in the Python programming language, it helps readers to develop technical computing skills in numerical and symbolic calculations, modeling and simulations, and visualizations. Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book can serve as an electrodynamics textbook for undergraduate physics and engineering students in their second or third years, who are studying intermediate- or advanced-level electrodynamics and who want to learn techniques for scientific computing at the same time. This book will also appeal to computer science students who want to see how their computer programming skills may be applied to science, particularly to physics, without needing too much background physics knowledge.Key features● Major concepts in classical electrodynamics are introduced cohesively through computational and mathematical treatments.● Computational examples in Python programming language guide students on how to simulate and visualize electrodynamic principles and phenomena for themselves.Taejoon Kouh is a Professor of Nano and Electronic Physics at Kookmin University, Republic of Korea. He earned his B.A. in physics from Boston University and Sc.M. and Ph.D. degrees in physics from Brown University. After his study in Providence, RI, he returned to Boston, MA, and worked as a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at Boston University. He is a full faculty member in the Department of Nano and Electronic Physics at Kookmin University in Seoul, Korea, teaching and supervising undergraduate and graduate students. His current research involves the dynamics of nanoelectromechanical systems and the development of fast and reliable transduction methods and innovative applications based on tiny motion.Minjoon Kouh is a program scientist for a philanthropic initiative. He was a Professor of Physics and Neuroscience at Drew University, USA, where he taught more than 30 distinct types of courses. He holds Ph.D. and B.S. degrees in physics from MIT and an M.A. from UC Berkeley. He completed a postdoctoral research fellowship at the Salk Institute for Biological Studies in La Jolla, CA. His research includes computational modeling of the primate visual cortex, information-theoretic analysis of neural responses, machine learning, and pedagogical innovations in undergraduate science education.
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Mathematical and Computer Programming Techniques for Computer Graphics
Provides a comprehensive and detailed coverage of the fundamentals of programming techniques for computer graphicsUses lots of code examples, encouraging the reader to explore and experiment with data and computer programs (in the C programming language)
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Programming
An Introduction to Programming by the Inventor of C++ Preparation for Programming in the Real World The book assumes that you aim eventually to write non-trivial programs, whether for work in software development or in some other technical field. Focus on Fundamental Concepts and Techniques The book explains fundamental concepts and techniques in greater depth than traditional introductions. This approach will give you a solid foundation for writing useful, correct, maintainable, and efficient code. Programming with Today’s C++ (C++11 and C++14) The book is an introduction to programming in general, including object-oriented programming and generic programming. It is also a solid introduction to the C++ programming language, one of the most widely used languages for real-world software. The book presents modern C++ programming techniques from the start, introducing the C++ standard library and C++11 and C++14 features to simplify programming tasks. For Beginners—And Anyone Who Wants to Learn Something New The book is primarily designed for people who have never programmed before, and it has been tested with many thousands of first-year university students. It has also been extensively used for self-study. Also, practitioners and advanced students have gained new insight and guidance by seeing how a master approaches the elements of his art. Provides a Broad View The first half of the book covers a wide range of essential concepts, design and programming techniques, language features, and libraries. Those will enable you to write programs involving input, output, computation, and simple graphics. The second half explores more specialized topics (such as text processing, testing, and the C programming language) and provides abundant reference material. Source code and support supplements are available from the author’s website.
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