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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.
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