Introduction To Python For Science And Engineering
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| Author | : David J. Pine |
| Publisher | : CRC Press |
| Total Pages | : 444 |
| Release | : 2024-09-23 |
| Genre | : Computers |
| ISBN | : 1040119573 |
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.
| Author | : Johnny Wei-Bing Lin |
| Publisher | : Cambridge University Press |
| Total Pages | : 767 |
| Release | : 2022-07-07 |
| Genre | : Computers |
| ISBN | : 1108701124 |
Textbook that uses examples and Jupyter notebooks from across the sciences and engineering to teach Python programming.
| Author | : Hans Petter Langtangen |
| Publisher | : Springer |
| Total Pages | : 942 |
| Release | : 2016-07-28 |
| Genre | : Computers |
| ISBN | : 3662498871 |
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
| Author | : Sandeep Nagar |
| Publisher | : Apress |
| Total Pages | : 264 |
| Release | : 2017-12-06 |
| Genre | : Computers |
| ISBN | : 1484232046 |
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
| Author | : Joakim Sundnes |
| Publisher | : |
| Total Pages | : 157 |
| Release | : 2020 |
| Genre | : Computer programming |
| ISBN | : 3030503569 |
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.
| Author | : John V. Guttag |
| Publisher | : MIT Press |
| Total Pages | : 466 |
| Release | : 2016-08-12 |
| Genre | : Computers |
| ISBN | : 0262529629 |
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.
| Author | : Svein Linge |
| Publisher | : Springer |
| Total Pages | : 244 |
| Release | : 2016-07-25 |
| Genre | : Computers |
| ISBN | : 3319324284 |
This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.
| Author | : John M. Stewart |
| Publisher | : Cambridge University Press |
| Total Pages | : 272 |
| Release | : 2017-07-20 |
| Genre | : Computers |
| ISBN | : 1316641236 |
Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively.
| Author | : Qingkai Kong |
| Publisher | : Academic Press |
| Total Pages | : 482 |
| Release | : 2020-11-27 |
| Genre | : Technology & Engineering |
| ISBN | : 0128195509 |
Python Programming and Numerical Methods: A Guide for Engineers and Scientists introduces programming tools and numerical methods to engineering and science students, with the goal of helping the students to develop good computational problem-solving techniques through the use of numerical methods and the Python programming language. Part One introduces fundamental programming concepts, using simple examples to put new concepts quickly into practice. Part Two covers the fundamentals of algorithms and numerical analysis at a level that allows students to quickly apply results in practical settings. - Includes tips, warnings and "try this" features within each chapter to help the reader develop good programming practice - Summaries at the end of each chapter allow for quick access to important information - Includes code in Jupyter notebook format that can be directly run online
| Author | : Alex Kenan |
| Publisher | : Alex Kenan |
| Total Pages | : 210 |
| Release | : 2021-01-01 |
| Genre | : Computers |
| ISBN | : 1736060600 |
The traditional computer science courses for engineering focus on the fundamentals of programming without demonstrating the wide array of practical applications for fields outside of computer science. Thus, the mindset of “Java/Python is for computer science people or programmers, and MATLAB is for engineering” develops. MATLAB tends to dominate the engineering space because it is viewed as a batteries-included software kit that is focused on functional programming. Everything in MATLAB is some sort of array, and it lends itself to engineering integration with its toolkits like Simulink and other add-ins. The downside of MATLAB is that it is proprietary software, the license is expensive to purchase, and it is more limited than Python for doing tasks besides calculating or data capturing. This book is about the Python programming language. Specifically, it is about Python in the context of mechanical and aerospace engineering. Did you know that Python can be used to model a satellite orbiting the Earth? You can find the completed programs and a very helpful 595 page NSA Python tutorial at the book’s GitHub page at https://www.github.com/alexkenan/pymae. Read more about the book, including a sample part of Chapter 5, at https://pymae.github.io
