Book Image

Scientific Computing with Python 3

By : Claus Führer, Jan Erik Solem, Olivier Verdier
Book Image

Scientific Computing with Python 3

By: Claus Führer, Jan Erik Solem, Olivier Verdier

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (23 chapters)
Scientific Computing with Python 3
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Acknowledgement
Preface
References

The array type


The objects used to manipulate vectors, matrices, and more general tensors in NumPy are called arrays. In this section, we examine their essential properties, how to create them, and how to access their information.

Array properties

Arrays are essentially characterized by three properties, which is given in the following table (Table 4.2):

Name

Description

shape

It describes how the data should be interpreted, as a vector, a matrix or as a higher order tensor, and it gives the corresponding dimension. It is accessed with the shape attribute.

dtype

It gives the type of the underlying data (float, complex, integer, and so on).

strides

This attribute specifies in which order the data should be read. For instance, a matrix could be stored in memory contiguously column by column (the FORTRAN convention), or row by row (the C convention). The attribute is a tuple with the numbers of bytes that have to be skipped in memory to reach the next row and the number of bytes...