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

12.1.3 Context managers – the with statement

There is a very useful construction in Python for simplifying exception handling when working with contexts such as files or databases. The statement encapsulates the structure try ... finally in one simple command. Here is an example of using with to read a file:

with open('data.txt', 'w') as f:
    process_file_data(f)

This will try to open the file, run the specified operations on the file (for example, reading), and close the file. If anything goes wrong during the execution of process_file_data, the file is closed properly and then the exception is raised. This is equivalent to:

f = open('data.txt', 'w')
try: 
    # some function that does something with the file 
    process_file_data(f) 
except:
    ... 
finally:
    f.close()

We will use this option in Section 14.1: File handling, when reading and writing files.

The preceding file-reading...