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

9.5.1 Generator expressions

There is an equivalent of list comprehension for generators. Such a construction is called a generator expression:

g = (n for n in range(1000) if not n % 100)
# generator for  100, 200, ... , 900

This is useful in particular for computing sums or products because those operations are incremental; they only need one element at a time:

sum(n for n in range(1000) if not n % 100) 
# returns 4500 (sum is here the built-in function)

In that code, you notice that the sum function is given one argument, which is a generator expression. Note that Python syntax allows us to omit the enclosing parentheses of generators when a generator is used as the only argument of a function.

Let's compute the Riemann zeta function , whose expression is

With a generator expression, we may compute a partial sum of this series in one line:

sum(1/n**s for n in itertools.islice(itertools.count(1), N))

Note that we could also have defined...