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

Accessing array entries


Array entries are accessed by indexes. In contrast to vector coefficients two indexes are needed to access matrix coefficients. These are given in one pair of brackets. This distinguishes the array syntax from a list of lists. There, two pairs of brackets are needed to access elements.

M = array([[1., 2.],[3., 4.]])
M[0, 0] # first row, first column: 1.
M[-1, 0] # last row, first column: 3.

Basic array slicing

Slices are similar to those of lists except that there might now be in more than one dimension:

  • M[i,:] is a vector filled by the row i of M.
  • M[:,j] is a vector filled by the column i of M.
  • M[2:4,:] is a slice of 2:4 on the rows only.
  • M[2:4,1:4] is a slice on rows and columns.

The result of matrix slicing is given in the following figure (Figure 4.1):

Figure 4.1: The result of matrix slicing

Note

Omitting a dimension

If you omit an index or a slice, NumPy assumes you are taking rows only. M[3] is a vector that is a view on the third row of and M[1:3] is a...