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

14.2.3 loadtxt

Reading to an array from a text file is done with the help of the following syntax:

filename = 'test.txt'
data = loadtxt(filename)

Due to the fact that each row in an array must have the same length, each row in the text file must have the same number of elements. Similar to savetxt, the default values are float and the delimiter is a space. These can be set using the parameters dtype and delimiter. Another useful parameter is comments, which can be used to mark what symbol is used for comments in the data file. An example of using the formatting parameters is as follows:

data = loadtxt('test.txt',delimiter=';')    # data separated by semicolons

# read to integer type, comments in file begin with a hash character
data = loadtxt('test.txt',dtype=int,comments='#')