Scientific Computing with Python: High-performance scientific computing with NumPy,SciPy,and pandas,2nd Edition 2nd edition
by: Claus Führer ，Jan Erik Solem，Olivier Verdier(Author)
Publisher finelybook 出版社: Packt Publishing; 2nd edition (July 23,2021)
Language 语言: English
Print Length 页数: 392 pages
ISBN-10: 1838822321
ISBN-13: 9781838822323
Book Description
By finelybook
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.
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
Table of Contents
Chapter 1: Getting Started
Chapter 2: Variables and Basic Tvoes
Chapter 3: Container Types
Chapter 4: Linear Algebra -Arrays
Chapter 5: Advanced Array Concepts
Chapter 6: Plotting
Chapter 7: Functions
Chapter 8: Classes
Chapter 9: Iterating
Chapter 10: Series and Dataframes -Working with Pandas
Chapter 11: Communication bv a Graphical User Interface
Chapter 12: Error and Exception Handling
Chapter 13: Namespaces,Scopes,and Modules
Chapter 14: Input and Outout
Chapter 15: Testing
Chapter 16: Symbolic Computations -SymPy
Chapter 17: Interacting with the Operating System
Chapter 18: Python for Parallel Computing
Chapter 19: Comprehensive Examples
About Packt
Other Books You May Enjoy
References
Index
