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

8.1.4 Attributes and methods

One of the main reasons for working with classes is that objects can be grouped together and bound to a common object. We saw this already when looking at rational numbers; denominator and numerator are two objects that we bound to an instance of the class RationalNumber. They are called attributes of the instance. The fact that an object is an attribute of a class instance becomes apparent from the way they are referenced, which we have used tacitly before:

​_<object>.attribute

Here are some examples of instantiation and attribute reference:

q = RationalNumber(3, 5) # instantiation
q.numerator              # attribute access
q.denominator

a = array([1, 2])        # instantiation
a.shape

z = 5 + 4j               # instantiationstepz.imag

Once an instance is defined, we can set, change, or delete attributes of that particular instance. The syntax is the same as for regular variables:

q = RationalNumber(3, 5) 
r = RationalNumber(7, 3)
q.numerator...