Packt - Scientific Computing With Python High Performance Scientific Computing With Numpy Scipy A...

  Scientific Computing With Python - Second Edition - High-Performance
  Scientific Computing With NumPy, SciPy, And Pandas
   LANGUAGE: English     ░   ISBN: 9781838822323      ░   REMOVED: DRM
https://www.worldcat.org/search?q=bn%3A9781838822323&fq=x0%3Abook+%2B+x4%3Adigit
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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 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: 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 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.

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