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

4.4.2 Altering an array using slices

You may alter an array using slices or by direct access. The following changes only one element in a  matrix :

M[1, 2] = 2.0 # scalar

Also, we may change one full row of the matrix:

M[2, :] = [1., 2., 3.] # vector

Similarly, we may also replace a full submatrix:

M[1:3, :] = array([[1., 2., 3.],[-1.,-2., -3.]])

There is a distinction between a column matrix and a vector. The following assignment with a column matrix returns no error: 

M[1:4, 1:2] = array([[1.],[0.],[-1.0]]) 

while the assignment with a vector returns a ValueError:

M[1:4, 1:2] = array([1., 0., -1.0]) #  error

The general slicing rules are shown in Table 4.3. The matrices and vectors in the preceding examples must have the right size to fit into matrix . You may also make use of the broadcasting rules (see Section 5.5Broadcasting) to determine the allowed size of the replacement arrays. If the replacement array does not have...