Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
NumPy Beginner's Guide

You're reading from   NumPy Beginner's Guide An action packed guide using real world examples of the easy to use, high performance, free open source NumPy mathematical library.

Arrow left icon
Product type Paperback
Published in Apr 2013
Publisher Packt
ISBN-13 9781782166085
Length 310 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Numpy Beginner's Guide Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. NumPy Quick Start FREE CHAPTER 2. Beginning with NumPy Fundamentals 3. Get in Terms with Commonly Used Functions 4. Convenience Functions for Your Convenience 5. Working with Matrices and ufuncs 6. Move Further with NumPy Modules 7. Peeking into Special Routines 8. Assure Quality with Testing 9. Plotting with Matplotlib 10. When NumPy is Not Enough – SciPy and Beyond 11. Playing with Pygame Pop Quiz Answers Index

Time for action – computing the simple moving average


The moving average is easy enough to compute with a few loops and the mean function, but NumPy has a better alternative—the convolve function. The simple moving average is, after all, nothing more than a convolution with equal weights or, if you like, unweighted.

Note

Convolution is a mathematical operation on two functions defined as the integral of the product of the two functions after one of the functions is reversed and shifted.

Use the following steps to compute the simple moving average:

  1. Use the ones function to create an array of size N and elements initialized to 1; then, divide the array by N to give us the weights, as follows:

    N = int(sys.argv[1])
    weights = np.ones(N) / N
    print "Weights", weights

    For N = 5, this code gives us the following output:

    Weights [ 0.2  0.2  0.2  0.2  0.2]
  2. Now call the convolve function with the following weights:

    c = np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
    sma = np.convolve(weights...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime