Python
Resources for research and practical applications of the Python programming language.
Researcher
History
A Brief History of the Python Programming Language
Python was created in the late 1980s by Guido van Rossum, a Dutch programmer working at Centrum Wiskunde & Informatica (CWI) in the Netherlands. Inspired by the ABC programming language, which was designed for teaching and prototyping but lacked extensibility, van Rossum aimed to develop a language that was both powerful and easy to use. In 1991, he released the first version of Python (Python 0.9.0), featuring core capabilities like exception handling, functions, and modules. The language emphasized readability, simplicity, and flexibility, making it attractive to both beginners and experienced programmers. Python 1.0 was officially released in 1994, introducing key features such as exception handling, lambda functions, and an interactive shell. The language gained early traction in academic and research communities. In 2000, Python 2.0 was released, adding list comprehensions and garbage collection based on reference counting. However, Python 2 had limitations, especially in handling Unicode, which became more apparent as the web grew. To address these issues, Python 3.0 was introduced in 2008, making significant improvements in Unicode support, library standardization, and syntax consistency. However, this version was not backward compatible with Python 2, leading to a gradual migration process that lasted until Python 2’s end-of-life in 2020. Today, Python is one of the most popular programming languages in the world. It is widely used in web development, data science, artificial intelligence, machine learning, automation, and scientific computing. With strong community support and continuous development, Python remains a dominant force in programming.
Books
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Python for Programmers
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The professional programmer's Deitel® guide to Python® with introductory artificial intelligence case studies Written for programmers with a background in another high-level language, Python for Programmers uses hands-on instruction to teach today's most compelling, leading-edge computing technologies and programming in Python--one of the world's most popular and fastest-growing languages. Please read the Table of Contents diagram inside the front cover and the Preface for more details. In the context of 500+, real-world examples ranging from individual snippets to 40 large scripts and full implementation case studies, you'll use the interactive IPython interpreter with code in Jupyter Notebooks to quickly master the latest Python coding idioms. After covering Python Chapters 1-5 and a few key parts of Chapters 6-7, you'll be able to handle significant portions of the hands-on introductory AI case studies in Chapters 11-16, which are loaded with cool, powerful, contemporary examples. These include natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop®, SparkTM and NoSQL databases, the Internet of Things and more. You'll also work directly or indirectly with cloud-based services, including Twitter, Google TranslateTM, IBM Watson, Microsoft® Azure®, OpenMapQuest, PubNub and more. Features 500+ hands-on, real-world, live-code examples from snippets to case studies IPython + code in Jupyter® Notebooks Library-focused: Uses Python Standard Library and data science libraries to accomplish significant tasks with minimal code Rich Python coverage: Control statements, functions, strings, files, JSON serialization, CSV, exceptions Procedural, functional-style and object-oriented programming Collections: Lists, tuples, dictionaries, sets, NumPy arrays, pandas Series & DataFrames Static, dynamic and interactive visualizations Data experiences with real-world datasets and data sources Intro to Data Science sections: AI, basic stats, simulation, animation, random variables, data wrangling, regression AI, big data and cloud data science case studies: NLP, data mining Twitter®, IBM® WatsonTM, machine learning, deep learning, computer vision, Hadoop®, SparkTM, NoSQL, IoT Open-source libraries: NumPy, pandas, Matplotlib, Seaborn, Folium, SciPy, NLTK, TextBlob, spaCy, Textatistic, Tweepy, scikit-learn®, Keras and more Register your product to gain access to updated chapters and material, as well as downloads, future updates, and/or corrections as they become available. See inside book for more information.
ISBN: 0135224330Publication Date: 2019-03-22 -
Starting Out with Python
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For courses in Python programming. A clear and student-friendly introduction to the fundamentals of Python In Starting Out with Python®, 4th Edition, Tony Gaddis' accessible coverage introduces students to the basics of programming in a high level language. Python, an easy-to-learn and increasingly popular object-oriented language, allows readers to become comfortable with the fundamentals of programming without the troublesome syntax that can be challenging for novices. With the knowledge acquired using Python, students gain confidence in their skills and learn to recognize the logic behind developing high-quality programs. Starting Out with Python discusses control structures, functions, arrays, and pointers before objects and classes. As with all Gaddis texts, clear and easy-to-read code listings, concise and practical real-world examples, focused explanations, and an abundance of exercises appear in every chapter. Updates to the 4th Edition include revised, improved problems throughout, and new Turtle Graphics sections that provide flexibility as assignable, optional material. Also Available with MyLab Programming. MyLabTM Programming is an online learning system designed to engage students and improve results. MyLab Programming consists of programming exercises correlated to the concepts and objectives in this book. Through practice exercises and immediate, personalized feedback, MyLab Programming improves the programming competence of beginning students who often struggle with the basic concepts of programming languages. Note: You are purchasing a standalone product; MyLab Programming does not come packaged with this content. Students, if interested in purchasing this title with MyLab Programming, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab Programming, search for: 0134543661 / 9780134543666 Starting Out with Python Plus MyLab Programming with Pearson eText -- Access Card Package, 4/e Package consists of: 0134444329 / 9780134444321 Starting Out with Python 0134484967 / 9780134484969 MyLab Programming with Pearson eText -- Access Code Card -- for Starting Out with Python Students can use the URL and phone number below to help answer their questions: http://247pearsoned.custhelp.com/app/home 800-677-6337
ISBN: 0134444329Publication Date: 2017-03-06 -
A Primer on Scientific Programming with Python
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The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python 'on the streets' could be a little jealous of students who have the opportunity to take a course out of Langtangen's Primer." John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small,IEEE, CiSE Vol. 14 (2), March /April 2012 "This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python..." Joan Horvath, Computing Reviews, March 2015
ISBN: 9783662498866Publication Date: 2016-08-01
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