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

16.1.1 Elaborating an example in SymPy

To begin with, let's elaborate on the previous example in SymPy and explain the steps.

First, we have to import the module:

from sympy import *
init_printing()

The second command makes sure that formulas are presented in a graphical way, if possible. Then, we generate a symbol and define the integrand:

x = symbols('x')
f = Lambda(x, 1/(x**2 + x + 1))

x is now a Python object of type Symbol and f is a SymPy Lambda function (note the command starting with a capital letter).

Now we start with the symbolic computation of the integral:

integrate(f(x),x)    

Depending on your working environment, the result is presented in different ways; see the following screenshot (Figure 16.2), which represents two different results of a SymPy formula in different environments:

Figure 16.2: Two screenshots of a SymPy presentation of formula in two different environments

We can check by differentiation...