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

Functions to construct arrays


The usual way to set up an array is via a list. But there are also a couple of convenient methods for generating special arrays, which are given in the following table (Table 4.5):

Methods

Shape

Generates

 zeros((n,m))

(n,m)

Matrix filled with zeros

ones((n,m)) 

(n,m)

Matrix filled with ones

diag(v,k) 

(n,n)

(Sub-, super-) diagonal matrix from a vector v

random.rand(n,m) 

(n,m)

Matrix filled with uniformly distributed random numbers in (0,1)

 arange(n)

(n,)

First n integers

linspace(a,b,n) 

(n,)

Vector with n equispaced points between a and b

Table 4.5: Commands to create arrays

These commands may take additional arguments. In particular, the commands zeros, ones, and arange take dtype as an optional argument. The default type is float, except for arange. There are also methods such as zeros_like and ones_like, which are slight variants of the preceding ones. For instance, the zeros_like(A) method is equivalent...