Leverage this example-packed, comprehensive guide for all your Python
computational needs
Key Features:
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Learn the first steps within Python to highly specialized concepts
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Explore examples and code snippets taken from typical programming
situations within scientific computing.
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Delve into essential computer science concepts like iterating,
object-oriented programming, testing, and MPI presented in strong
connection to applications within scientific computing.
Book Description:
Python 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:
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Understand the building blocks of computational mathematics, linear
algebra, and related Python objects
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Use Matplotlib to create high-quality figures and graphics to draw and
visualize results
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Apply object-oriented programming (OOP) to scientific computing in
Python
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Discover how to use pandas to enter the world of data processing
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Handle exceptions for writing reliable and usable code
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Cover manual and automatic aspects of testing for scientific
programming
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Get to grips with parallel computing to increase computation speed
Who this book is for:
This 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.
