Search results for "Python in Depth"
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R 947.00
Introduction to Psychophysics with Python
Introduction to Psychophysics with Python is a hands-on guide to exploring how we sense and perceive the world—powered by modern Python programming. Bridging classical theory with today’s computational tools, it equips students and researchers to design, run, and analyze psychophysical experiments with confidence.Starting from the foundations laid by Weber, Fechner, and Stevens, the book traces the evolution of psychophysics into contemporary practice. Readers gain a clear grounding in sensory physiology, experimental design, and statistical analysis, always tied to the central challenge: connecting physical stimuli to perceptual responses and making sense of the data.What sets this book apart is its practical, step-by-step approach. Through tutorials and annotated code using PsychoPy, readers learn Python essentials—loops, conditionals, data handling—while building real experiments. These skills extend seamlessly into advanced methods like curve fitting, bootstrapping, and statistical inference.Illustrated with intuitive explanations, diagrams, and thought-provoking exercises, the book blends conceptual clarity with real-world application. By the end, readers will not only understand the principles of psychophysics but also have the coding expertise to create and analyze their own experiments—opening the door to deeper insights into sensation and perception.
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Introduction to Programming Concepts with Case Studies in Python
The current text provides a clear introduction to Computer Science concepts in a programming environment. It is designed as suitable use in freshman- or introductory level coursework in CS and provides the fundamental concepts as well as abstract theorems for solving computational problems. The Python language serves as a medium for illustrating and demonstrating the concepts.
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Grundlagen der Python-Programmierung
Dieses Buch vermittelt einen systematischen Einstieg in die Python-Programmierung und führt Schritt für Schritt durch alle wichtigen Grundlagen bis hin zu fortgeschrittenen Konzepten. Es beginnt mit den zentralen Sprachelementen wie Datentypen, Variablen, Operatoren, Zeichenketten und numerischen Funktionen und zeigt anhand klarer Beispiele, wie Python effizient eingesetzt wird.Aufbauend darauf behandelt das Buch strukturierte Daten wie Listen, Tupel, Dictionaries und Sets sowie Kontrollstrukturen mit Verzweigungen und Schleifen. Im weiteren Verlauf werden Funktionen, Fehlerbehandlung und objektorientierte Programmierung ausführlich erklärt. Ein eigenes Kapitel widmet sich dem Arbeiten mit Dateien, inklusive Lesen, Schreiben, CSV-Verarbeitung und grundlegenden Funktionen des Betriebssystems.Abgerundet wird das Werk durch einen großen Übungsteil mit praxisnahen Aufgaben und vollständigen Lösungen, die das eigenständige Lernen unterstützen und den Stoff festigen. Damit eignet sich das Buch sowohl für Einsteigerinnen und Einsteiger in Informatik und Data Science als auch für Anwenderinnen und Anwender, die ein solides Fundament für weiterführende Python-Anwendungen aufbauen möchten.
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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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Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
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Quick Python 3
Are you a programmer who wants to get started quickly in a new language? This book is for you.Are you a novice who wants to learn to program? This book is not for you. Are you a Python programmer who needs encyclopaedic information? This book is not for you.Like any mainstream language, Python has loops, if statements, assignment statements, functions, etc. I’ll show you what these look like in Python. I won’t waste your time telling you what they’re good for. Python has features you may not be familiar with--iterators, list comprehensions, maybe even dictionaries. I’ll spend more time on these. I’ll cover some of the library functions I found most immediately useful, and tell you where to find more. In short, this book will help you hit the ground running. Next week, you’ll be ready to buy that Python encyclopaedia.
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Lean Python
Learn only the essential aspects of Python without cluttering up your mind with features you may never use. This compact book is not a "best way to write code" type of book; rather, the author goes over his most-used functions, which are all you need to know as a beginner and some way beyond.Lean Python takes 58 Python methods and functions and whittles them down to 15: as author Paul Gerrard says, "I haven't found a need for the rest."What You'll LearnDiscover lean Python and how to learn just enough to build useful toolsUse Python objects, program structure, I/O, modules and moreHandle errors and exceptions Test your codeAccess the Web; do searching; and persist dataWho This Book Is ForThis book is aimed at three categories of reader: The experienced programmer – if you already know a programming language, this book gives you a shortcut to understanding the Python language and some of its design philosophy. You work in IT and need a programming primer – you might be a tester who needs to have more informed technical discussions with programmers. Working through the examples will help you to appreciate the challenge of good programming. First-timer – you want a first book on programming that you can assimilate quickly to help you decide whether programming is for you.
R 987.60
Python Projects for Beginners
Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects.Each chapter in this book is presented as a full week of topics, with Monday through Thursday covering specific concepts, leading up to Friday, when you are challenged to create a project using the skills learned throughout the week. Topics include Python basics and essential intermediate concepts such as list comprehension, generators and iterators, understanding algorithmic complexity, and data analysis with pandas. From beginning to end, this book builds up your abilities through exercises and challenges, culminating in your solid understanding of Python.Challenge yourself with the intensity of a coding bootcamp experience or learn at your own pace. With this hands-on learning approach, you will gain the skills you need to jumpstart a new career in programming or further your current one as a software developer. What You Will LearnUnderstand beginning and more advanced concepts of the Python languageBe introduced to data analysis using pandas, the Python Data Analysis libraryWalk through the process of interviewing and answering technical questionsCreate real-world applications with the Python languageLearn how to use Anaconda, Jupyter Notebooks, and the Python ShellWho This Book Is ForThose trying to jumpstart a new career into programming, and those already in the software development industry and would like to learn Python programming.
R 1,322.57
50 Algorithms Every Programmer Should Know
Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not n
R 1,628.00
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
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Python Quick Syntax Reference
The Python Quick Syntax Reference is the "go to" book that contains an easy to read and useguide to Python programming and development. This condensed code and syntaxreference presents the Python language in a well-organized format designed tobe used time and again.You won't find jargon, bloated samples, case studies, or history of Hello Worldand computer theory in this handy reference. This Python syntax reference ispacked with useful information and is a must-have for any Python developer.
R 1,262.73
Python for Data Analysis 3e
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the Jupyter notebook and IPython shell for exploratory computingLearn basic and advanced features in NumPyGet started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples
R 1,978.00
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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Mastering Python for Bioinformatics
Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools - including linters, formatters, type checkers, and tests - to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.Create command-line Python programs to document and validate parametersWrite tests to verify refactor programs and confirm they're correctAddress bioinformatics ideas using Python data structures and modules such as BiopythonCreate reproducible shortcuts and workflows using makefilesParse essential bioinformatics file formats such as FASTA and FASTQFind patterns of text using regular expressionsUse higher-order functions in Python like filter(), map(), and reduce()
R 2,280.00
Object-Oriented Approach to Problem Solving and Machine Learning with Python
This book is a comprehensive guide suitable for beginners and experienced developers alike. It teaches readers how to master object-oriented programming (OOP) with Python and use it in real-world applications.Start by solidifying your OOP foundation with clear explanations of core concepts such as use cases and class diagrams. This book goes beyond theory as you get practical examples with well-documented source code available in the book and on GitHub.This book doesn’t stop at the basics. Explore how OOP empowers fields such as data persistence, graphical user interfaces (GUIs), machine learning, and data science, including social media analysis. Learn about machine learning algorithms for classification, regression, and unsupervised learning, putting you at the forefront of AI innovation.Each chapter is designed for hands-on learning. You’ll solidify your understanding with case studies, exercises, and projects that apply your newfound knowledge to real-world scenarios. The progressive structure ensures mastery, with each chapter building on the previous one, reinforced by exercises and projects.Numerous code examples and access to the source code enhance your learning experience. This book is your one-stop shop for mastering OOP with Python and venturing into the exciting world of machine learning and data science.
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