Search results for "Andre Python"
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R 687.00
R 1,279.45
Python for Teenagers
This book, which is designed for middle-school through college-aged students, will arm beginners with solid programming foundations they can carry throughout life. It uses fun and simple language (and programming examples) to teach the fundamentals needed to start the down path of becoming a programmer. Python is a highly flexible language, allowing developers to enter any number of technical fields and is a welcome addition to any resume. With its low learning curve, it makes a great introductory language, as new developers can take the coding fundamentals they learn in Python and apply them to any other language. The second edition builds upon the foundation of the first book, revising all the chapters where the language has changed, updating the commands, code, and examples to bring it up to date with the current version of Python. Since Python is the most popular programming language in the world and can be used in conjunction with other languages - across multiple platforms - it can increase the reader's ability to qualify for a wider range of jobs than other languages. Finally, Python is fun - something not every programming language can boast! What You Will LearnInstall and configure PythonGrasp basic software development principles and syntaxUnderstand the best practices for coding in PythonCreate applications and debug code Who This Book Is ForThe book’s target audience is primarily middle school to college aged students looking to learn how to program computers and develop software. Older individuals and computer programmers who know other languages and want to add Python to their repertoire can also benefit from the book.
R 798.00
R 352.00
Data Structures and Algorithms in Python
Based on the authors’ market leading data structures books in Java and C++, this textbook offers a comprehensive, definitive introduction to data structures in Python by respected authors. Data Structures and Algorithms in Python is the first mainstream object-oriented book available for the Python data structures course. Designed to provide a comprehensive introduction to data structures and algorithms, including their design, analysis, and implementation, the text will maintain the same general structure as Data Structures and Algorithms in Java and Data Structures and Algorithms in C++.
R 5,442.00
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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Bayesian Analysis with Python
Bayesian modeling with PyMC3 and exploratory analysis of Bayesian models with ArviZ Key FeaturesA step-by-step guide to conduct Bayesian data analyses using PyMC3 and ArviZA modern, practical and computational approach to Bayesian statistical modelingA tutorial for Bayesian analysis and best practices with the help of sample problems and practice exercises.Book DescriptionThe second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.What you will learnBuild probabilistic models using the Python library PyMC3 Analyze probabilistic models with the help of ArviZ Acquire the skills required to sanity check models and modify them if necessary Understand the advantages and caveats of hierarchical modelsFind out how different models can be used to answer different data analysis questionsCompare models and choose between alternative onesDiscover how different models are unified from a probabilistic perspective Think probabilistically and benefit from the flexibility of the Bayesian frameworkWho this book is forIf you are a student, data scientist, researcher, or a developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory so no previous statistical knowledge is required, although some experience in using Python and NumPy is expected.
R 1,661.00
Testing Python
Fundamental testing methodologies applied to the popular Python language Testing Python; Applying Unit Testing, TDD, BDD and Acceptance Testing is the most comprehensive book available on testing for one of the top software programming languages in the world. Python is a natural choice for new and experienced developers, and this hands-on resource is a much needed guide to enterprise-level testing development methodologies. The book will show you why Unit Testing and TDD can lead to cleaner, more flexible programs. Unit Testing and Test-Driven Development (TDD) are increasingly must-have skills for software developers, no matter what language they work in. In enterprise settings, it's critical for developers to ensure they always have working code, and that's what makes testing methodologies so attractive. This book will teach you the most widely used testing strategies and will introduce to you to still others, covering performance testing, continuous testing, and more. Learn Unit Testing and TDD—important development methodologies that lie at the heart of Agile developmentEnhance your ability to work with Python to develop powerful, flexible applications with clean codeDraw on the expertise of author David Sale, a leading UK developer and tech commentatorGet ahead of the crowd by mastering the underappreciated world of Python testing Knowledge of software testing in Python could set you apart from Python developers using outmoded methodologies. Python is a natural fit for TDD and Testing Python is a must-read text for anyone who wants to develop expertise in Python programming.
R 1,026.00
R 458.00
Python
Python's simplicity and versatility make it an ideal language for both beginners and experienced programmers. Its syntax facilitates a smooth learning curve, enabling individuals to concentrate on grasping programming concepts instead of wrestling with intricate syntax rules. The extensive standard library reinforces its practicality, offering pre-built modules and functions that reduce manual coding efforts. Python's versatility is evident in its applications, spanning web development, data analysis, machine learning, and automation. The language's interactive nature supports real-time code experimentation, stepping up the learning process and enhancing understanding. Python's wealth of online resources further enriches the learning experience, fostering a community where individuals can develop their programming skills. The book, "Python - A Practical Learning Approach," exemplifies Python's simplicity and versatility with numerous examples, ensuring a seamless learning journey. Beyond theory, the language's practicality allows learners to actively apply their knowledge in real-world scenarios, establishing Python as an asset in education.
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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.
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Head First Python
What will you learn from this book? Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you'll quickly grasp Python's fundamentals by working with built-in data structures and functions. You'll build your very own web app, which--once it's ready for prime time--runs in the cloud. You'll learn how to wrangle data with Python, scrape data from the web, feed data to pandas, and interact with databases. This third edition is a complete learning experience that will help you become a bona fide Python programmer in no time. What's so special about this book? If you've read a Head First book, you know what to expect: a visually rich format designed for the way your brain works. If you haven't, you're in for a treat. With this book, you'll learn Python through a multisensory experience that engages your mind--rather than a text-heavy approach that puts you to sleep.
R 1,505.00
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.
R 2,082.80
Python Book
The Python Book Discover the power of one of the fastest growing programming languages in the world with this insightful new resource The Python Book delivers an essential introductory guide to learning Python for anyone who works with data but does not have experience in programming. The author, an experienced data scientist and Python programmer, shows readers how to use Python for data analysis, exploration, cleaning, and wrangling. Readers will learn what in the Python language is important for data analysis, and why. The Python Book offers readers a thorough and comprehensive introduction to Python that is both simple enough to be ideal for a novice programmer, yet robust to be useful for those more experienced in the language. The book assists budding programmers to gradually increase their skills as they move through the book, always with an understanding of what they are covering and why it is useful. Used by major companies like Google, Facebook, Instagram, Spotify, and more, Python promises to remain central to the programming landscape for years to come. Containing a thorough discussion of Python programming topics like variables, equalities and comparisons, tuple and dictionary data types, while and for loops, and if statements, readers will also learn: How to use highly useful Python programming libraries, including Pandas and MatplotlibHow to write Python functions and classesHow to write and use Python scriptsTo deal with different data types within Python Perfect for statisticians, computer scientists, software programmers, and practitioners working in private industry and medicine, The Python Book will also be of interest to students in any of the aforementioned fields. As it assumes no programming experience or knowledge, the book is ideal for those who work with data and want to learn to use Python to enhance their work.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 3,079.26
R 888.00
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.
R 3,814.47
Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
R 7,787.80
How to code in Python: GCSE, iGCSE, National 4/5 and Higher
Ensure every student can become fluent in Python with this highly practical guide that will help them understand the theory and logic behind coding.Written for 14-16-year olds by a leading Python specialist and teacher, and aligned to curriculum requirements, this essential Student Book provides numerous practice questions and coding problems that can be completed as homework or during class - plus answers can be found online at www.hoddereducation.co.uk/pythonextrasHow to Code in Python will: Provide hundreds of coding examples, puzzles and problem-solving tasks to strengthen computational thinking skills required for GCSE, iGCSE and National 4 / 5 success Help students become proficient in computational thinking and problem-solving using Python Provide easy-to-follow explanations of concepts and terminology Feature plenty of opportunities for self-assessment with solutions to coding problems available onlineThis unique book can be broken down into three key features: Code theory and explanations (worked examples) in a fun and accessible way Computational thinking puzzles for the reader to solve; this will greatly improve students' ability to read code and predict its effect and output when run Programming problems where the reader has to write a program to solve given scenariosiGreg Reid is a very experienced Computer Science teacher in Scotland, who has written How to Pass Higher Computer Science and Higher Computing Science Practice Papers for Hodder Gibson./i/p
R 953.00
R 519.00
Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
R 2,585.49
R 607.00
Python for Marketing Research and Analytics
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
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