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Scientific Computing with Python - High-performance scientific computing with NumPy, SciPy, and pandas (Paperback, 2nd Revised edition)
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Scientific Computing with Python - High-performance scientific computing with NumPy, SciPy, and pandas (Paperback, 2nd Revised edition)
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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.
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