Python From The Very Beginning

In Python from the Very Beginning John Whitington takes a no-prerequisites approach to teaching the basics of a modern general-purpose programming language. Each small, self-contained chapter introduces a new topic, building until the reader can write quite substantial programs. There are plenty of questions and, crucially, worked answers and hints. Python from the Very Beginning will appeal both to new programmers, and to experienced programmers eager to explore functional languages such as Haskell. It is suitable both for formal use within an undergraduate or graduate curriculum, and for the interested amateur.

This tutorial offers readers a thorough introduction to programming in Python 2.4, the portable, interpreted, object-oriented programming language that combines power with clear syntax Beginning programmers will quickly learn to develop robust, reliable, and reusable Python applications for Web development, scientific applications, and system tasks for users or administrators Discusses the basics of installing Python as well as the new features of Python release 2.4, which make it easier for users to create scientific and Web applications Features examples of various operating systems throughout the book, including Linux, Mac OS X/BSD, and Windows XP

* Totaling 900 pages and covering all of the topics important to new and intermediate users, Beginning Python is intended to be the most comprehensive book on the Python ever written. * The 15 sample projects in Beginning Python are attractive to novice programmers interested in learning by creating applications of timely interest, such as a P2P file-sharing application, Web-based bulletin-board, and an arcade game similar to the classic Space Invaders. * The author Magnus Lie Hetland, PhD, is author of Apress’ well-received 2002 title, Practical Python, ISBN: 1-59059-006-6. He’s also author of the popular online guide, Instant Python Hacking (http://www.hetland.org), from which both Practical Python and Beginning Python are based.

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.

In OCaml from the Very Beginning John Whitington takes a no-prerequisites approach to teaching a modern general-purpose programming language. Each small, self-contained chapter introduces a new topic, building until the reader can write quite substantial programs. There are plenty of questions and, crucially, worked answers and hints. OCaml from the Very Beginning will appeal both to new programmers, and experienced programmers eager to explore functional languages such as OCaml. It is suitable both for formal use within an undergraduate or graduate curriculum, and for the interested amateur.

In Haskell from the Very Beginning John Whitington takes a no-prerequisites approach to teaching the basics of a modern general-purpose programming language. Each small, self-contained chapter introduces a new topic, building until the reader can write quite substantial programs. There are plenty of questions and, crucially, worked answers and hints. Haskell from the Very Beginning will appeal both to new programmers, and to experienced programmers eager to explore functional languages such as Haskell. It is suitable both for formal use within an undergraduate or graduate curriculum, and for the interested amateur.

Beginning Python Games Development, Second Edition teaches you how to create compelling games using Python and the PyGame games development library. It will teach you how to create visuals, do event handling, create 3D games, add media elements, and integrate OpenGL into your Python game. In this update to the first ever book to cover the popular open source PyGame games development library, you'll stand to gain valuable technical insights and follow along with the creation of a real-world, freely downloadable video game. Written by industry veterans and Python experts Will McGugan and Harrison Kinsley, this is a comprehensive, practical introduction to games development in Python. You can also capitalize upon numerous tips and tricks the authors have accumulated over their careers creating games for some of the world's largest game developers.

The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.