Python (R) is a free open-source language and environment that has tremendous potential in the scientific computing domain. Computing with Python presents the programming language in tight connection with mathematical applications. The approach of the book is concept based rather than a systematic introduction to the language. It is written for a mathematical readership and is aimed at students with a mathematical background.

The important interaction between modeling and solution techniques is demonstrated by using a simplified multibody model of a truck thoughout the book to illustrate all key concepts.

Leverage this example-packed, comprehensive guide for all your Python computational needs Key Features Learn the first steps within Python to highly specialized concepts Explore examples and code snippets taken from typical programming situations within scientific computing. Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing. Book DescriptionPython has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing. What you will learn Understand the building blocks of computational mathematics, linear algebra, and related Python objects Use Matplotlib to create high-quality figures and graphics to draw and visualize results Apply object-oriented programming (OOP) to scientific computing in Python Discover how to use pandas to enter the world of data processing Handle exceptions for writing reliable and usable code Cover manual and automatic aspects of testing for scientific programming Get to grips with parallel computing to increase computation speed Who this book is forThis book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.

An example-rich, comprehensive guide for all of your Python computational needs About This Book * Your ultimate resource for getting up and running with Python numerical computations * Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules * A hands-on guide to implementing mathematics with Python, with complete coverage of all the key concepts Who This Book Is For This book is for anyone who wants to perform numerical and mathematical computations in Python. It is especially useful for developers, students, and anyone who wants to use Python for computation. Readers are expected to possess basic a knowledge of scientific computing and mathematics, but no prior experience with Python is needed. What You Will Learn * The principal syntactical elements of Python * The most important and basic types in Python * The essential building blocks of computational mathematics, linear algebra, and related Python objects * Plot in Python using matplotlib to create high quality figures and graphics to draw and visualize your results * Define and use functions and learn to treat them as objects * How and when to correctly apply object-oriented programming for scientific computing in Python * Handle exceptions, which are an important part of writing reliable and usable code * Two aspects of testing for scientific programming: Manual and Automatic In Detail Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more. Style and approach This book takes a concept-based approach to the language rather than a systematic introduction. It is a complete Python tutorial and introduces computing principles, using practical examples to and showing you how to correctly implement them in Python. You'll learn to focus on high-level design as well as the intricate details of Python syntax. Rather than providing canned problems to be solved, the exercises have been designed to inspire you to think about your own code and give you real-world insight.