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This easy-to-follow and classroom-tested textbook guides the reader through the fundamentals of programming with Python, an accessible language which can be learned incrementally. Features: incudes numerous examples and practice exercises throughout the text, with additional exercises, solutions and review questions at the end of each chapter; highlights the patterns which frequently appear when writing programs, reinforcing the application of these patterns for problem-solving through practice exercises; introduces the use of a debugger tool to inspect a program, enabling students to discover for themselves how programs work and enhance their understanding; presents the Tkinter framework for building graphical user interface applications and event-driven programs; provides instructional videos and additional information for students, as well as support materials for instructors, at an associated website.
If you want to learn how to program, working with Python is an excellent way to start. This hands-on guide takes you through the language a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. This second edition and its supporting code have been updated for Python 3. Through exercises in each chapter, youâ??ll try out programming concepts as you learn them. Think Python is ideal for students at the high school or college level, as well as self-learners, home-schooled students, and professionals who need to learn programming basics. Beginners just getting their feet wet will learn how to start with Python in a browser. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand objects, methods, and object-oriented programming Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design, data structures, and GUI-based programs through case studies
Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.Python is an easy to use and easy to learn programming language that is freely available on Macintosh, Windows, or Linux computers. So once you learn Python you can use it for the rest of your career without needing to purchase any software.This book uses the Python 3 language. The earlier Python 2 version of this book is titled "Python for Informatics: Exploring Information".There are free downloadable electronic copies of this book in various formats and supporting materials for the book at www.pythonlearn.com. The course materials are available to you under a Creative Commons License so you can adapt them to teach your own Python course.
"The user-friendly, object-oriented programming language Python is quickly becoming the most popular introductory programming language for both students and instructors ... Building on essential concepts of computer science and offering a plentitude of real-world examples, Python programming in context, Second edition offers a thorough overview of multiple applied areas, including image processing, cryptography, astronomy, the Internet, and bioinformatics. The text's emphasis on problem solving, extrapolation, and development of independent exploration and solution building provides students with a unique and innovative approach to learning programming." --
"Python 3 Fundamentals: A Complete Guide for Modern Programmers" is an authoritative resource designed to equip both novice and experienced developers with a thorough understanding of Python programming. Written by an expert in computer science and software engineering, this comprehensive guide navigates through essential Python topics, providing readers with a definitive pathway to mastery. From setting up the Python environment and understanding variables and data types, to exploring control flow, functions, and data structures, every chapter is meticulously crafted to offer clear, insightful explanations alongside practical examples. Delving deeper, the book expands on advanced concepts such as object-oriented programming, exception handling, and file management, ensuring readers gain a solid foundation in developing scalable, efficient Python applications. With sections dedicated to leveraging Python's expansive libraries and frameworks, as well as integrating best practices for testing and debugging, this guide is not only a learning tool but also a valuable reference for creating robust, high-quality software. Whether you're building web applications, automating tasks, or embarking on data analysis, this guide empowers you with the skills needed to harness the full potential of Python in any domain.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Free Open Source Software have been growing enormously in the field of information technology. Open Source Software (OSS) is a software whose source code is accessible for alteration or enrichment by other programmers. This book gives a detailed analysis of open source software and their fundamentals, and so is meant for the beginners who want to learn and write programs using Open Source Software. It also educates on how to download and instal these open source free software in the system.The topics covered in the book broadly aims to develop familiar Open Source Software (OSS) associated with database, web portal and scientific application development. Software platforms like, Android, MySQL, PHP, Python, PERL, Grid Computing, and Open Source Cloud, and their applications are explained through various examples and programs. The platforms like OSS and Linux are also introduced in the book. Recapitulation given at the end of each chapter enables the readers to take a quick revision of the topics. Numerous examples in the form of programs are given to enable the students to understand the theoretical concepts and their applicative knowledge. The book is an introductory textbook on Open Source Software (OSS) for the undergraduate students of Computer Science Engineering (CSE) and postgraduate students of Computer Application (MCA). Salient Features The procedure for installing software (Linux, Android, PHP, MySQL, Perl, and Python) both in Linux and Windows operating systems are discussed in the book.• Numerous worked out example programs are introduced.• Inclusion of several questions drawn from previous question papers in chapter-end exercises.
Written for computer programming students, hobbyists, and professionals, FUNDAMENTALS OF PYTHON: DATA STRUCTURES is an introduction to object-oriented design and data structures using the popular Python programming language. The level of instruction assumes at least one semester of programming in an object-oriented language such as Java, C++, or Python. Through the step-by-step instruction and exercises in this book, you'll cover such topics as the design of collection classes with polymorphism and inheritance, multiple implementations of collection interfaces, and the analysis of the space/time tradeoffs of different collection implementations (specifically array-based implementations and link-based implementations). Collections covered include sets, lists, stacks, queues, trees, dictionaries, and graphs. Get ready to dig into Python data structures with FUNDAMENTALS OF PYTHON: DATA STRUCTURES.
Want complete instructions on the Python library and its elements? Get solutions with practical case studies and implications of python in data analysis through this book. “A BEGINNER’S GUIDE TO PYTHON FOR DATA ANALYTICS” will help you to learn about the different aspects of python along with its implementation in data analysis in different industries.