Book Image

Scientific Computing with Python - Second Edition

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python - Second Edition

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

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.
Table of Contents (23 chapters)
20
About Packt
22
References

4.8.2 Array functions

There are a number of functions acting on arrays that do not act componentwise. Examples of such functions are max, min, and sum. These functions may operate on the entire matrix, row-wise, or column-wise. When no argument is provided, they act on the entire matrix.

Suppose:

The function sum acting on that matrix returns a scalar:

sum(A) # 36

The command has an optional parameter, axis. It allows us to choose along which axis to perform the operation. For instance, if the axis is , it means that the sum should be computed along the first axis. The sum along axis  of an array of shape  will be a vector of length .

Suppose we compute the sum of A along the axis :

sum(A, axis=0) # array([ 6, 8, 10, 12])

This amounts to computing the sum on the columns:

The result is a vector:

Now suppose we compute the sum along axis 1:

A.sum(axis=1) # array([10, 26])

This amounts to computing...