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R 1,400.00
Python for non-Pythonians
The book uses a very simple and accessible language. All the descriptions of Python functionalities come with intuitive examples to make you learn by doing. This is not a theoretical book and does not cover some of the most internal features of Python. The intention of the authors is to allow business oriented people to start using Python. The main reason for such a choice of style is due to the increasing number of requests by non-technical professionals to solve daily problems and tasks. Whether we want to append multiple spreadsheets or profile the customer base, being able to use a solid infrastructure which enables to collect, check, process, analyze data, and report results has become a basic requirement in most industries. The structure of this book replicates the structure of a grammar book. It first introduces the building blocks of Python - i.e., objects - which can be compared to the different types of words (e.g., verbs, nouns) of a language. It introduces readers to the syntax rules of Python - i.e., statements, functions, and classes. It then provides introduces how to use this new language for managing data. One thing that we found particularly helpful for our students is to provide wrong Python codes with the error message that Python provides, and ask them to fix the code in order to make it work. As making mistakes is very common, we would say normal, in programming, learning how to interpret the error messages and fix codes is extremely important from a pedagogical perspective. The book always challenges students with a series of "fix the code" questions at the end of each a topic. We adopt this structure because we found that students learn how to successfully write algorithms in Python when they understand its logic and the meaning of every single element of the algorithm. We have experimented that this natural progression, from the most basic elements to very complex scripts, raises students' self-confidence and proficiency in writing their own algorithms. Hence, the structure of the textbook greatly helps students and instructors to gradually acquire the "programming" mindset. Also, our teaching experience indicates that, as with any other language, students learn by practicing. For this reason our textbook, is rich in exercises of increasing complexity.
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AI-Assisted Python for Nonprogrammers
It's hard to exaggerate Python's popularity: It's used for everything from data science to servers to the web. It's also famously "easy" to learn. However, learning to program in Python can still be a frustrating experience. AI-Assisted Python for Nonprogrammers is designed to give you the context you need to build a deep understanding not just of Python's syntax but also of the concepts behind it, even if you don't have any previous programming experience.Through clear explanations and explorations, author Reuven Lerner will have you writing simple Python programs in no time. By collaborating with AI chatbots to complete focused hands-on exercises, you'll reinforce core concepts like variables and values, conditions and loops, functions, and modules (while also getting a taste of coding with GenAI). But most importantly, you'll learn how to think about computers and software like a programmer—setting you up to better participate in technical conversations at your job and understand the influence of technology on our world.Write simple Python programs using core data structures, conditions, and loopsWrite and use Python functionsUse Python modulesGain the confidence to continue your Python journey
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Simple Introduction to Python
A Simple Introduction to Python is aimed at pre-university students and complete novices to programming. The whole book has been created using Jupyter notebooks. After introducing Python as a powerful calculator, simple programming constructs are covered, and the NumPy, MatPlotLib and SymPy modules (libraries) are introduced. Python is then used for Mathematics, Cryptography, Artificial Intelligence, Data Science and Object Oriented Programming.The reader is shown how to program using the integrated development environments: Python IDLE, Spyder, Jupyter notebooks, and through cloud computing with Google Colab.Features:No prior experience in programming is required.Demonstrates how to format Jupyter notebooks for publication on the Web.Full solutions to exercises are available as a Jupyter notebook on the Web.All Jupyter notebook solution files can be downloaded through GitHub.GitHub Repository of Data Files and a Jupyter Solution notebook: https://github.com/proflynch/A-Simple-Introduction-to-PythonJupyter Solution notebook web page: https://drstephenlynch.github.io/webpages/A-Simple-Introduction-to-Python-Solutions.html
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Introduction to Python
Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.FeaturesCovers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programmingSuitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionalsSolutions manual, code, and additional examples are available for download
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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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CPython
Deep Dive CPython explores the internal mechanics of CPython, the widely used Python interpreter written in C. Starting with a practical guide on downloading and compiling the CPython source, this book is perfect for developers eager to understand Python's behaviour at a fundamental level. The book takes readers from basic concepts to complex details with a systematic breakdown of core components. It covers everything from CPython's data structures like PyObject and PyTypeObject to object lifecycle management, giving insight into memory allocation and object reference counting. Each chapter illustrates CPython's architecture, such as Python's "everything is an object" philosophy, list handling, string manipulation, and dictionary operations. Readers will explore Python's REPL modifications, string internals, and custom type creation with practical examples, like crafting a "backflipping" PyKittyType. Detailed sections on Python's virtual machine operations, bytecode generation, and exception handling enrich readers' understanding of how Python code is parsed, compiled, and executed. This book is a thorough guide for readers who want to go beyond basic Python use and understand how it works internally. Covering complex concepts like generators, iterators, descriptors, and metaclasses, this book equips readers with a thorough grasp of Python's performance optimization and design complexities. What you will learn: How to download, compile, and modify CPython's source codeGain insight into fundamental structures like PyObject and PyTypeObject, Understand Python's detailed handling of lists, strings, dictionaries, and the REPL environment.What are bytecode generation, custom types, and the inner workings of Python's virtual machine. Who this book is for: Python programmers aiming to gain a deeper understanding of Python's internals and move beyond standard usage, as well as software professionals interested in CPython's C-based implementation and core architecture.
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