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Learning NumPy Array

You're reading from   Learning NumPy Array Supercharge your scientific Python computations by understanding how to use the NumPy library effectively

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783983902
Length 164 pages
Edition Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (14) Chapters Close

Learning NumPy Array
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started with NumPy FREE CHAPTER 2. NumPy Basics 3. Basic Data Analysis with NumPy 4. Simple Predictive Analytics with NumPy 5. Signal Processing Techniques 6. Profiling, Debugging, and Testing 7. The Scientific Python Ecosystem Index

Indexing arrays with Booleans


Boolean indexing is indexing based on a Boolean array and falls in the category of fancy indexing. Since Boolean indexing is a form of fancy indexing, the way it works is basically the same. This means that indexing happens with the help of a special iterator object. Perform the following steps to index an array:

  1. First, we create an image with dots on the diagonal. This is in some way similar to the Fancy indexing section. This time we select modulo four points on the diagonal of the image, as shown in the following code snippet:

    def get_indices(size):
       arr = np.arange(size)
       return arr % 4 == 0
  2. Then we just apply this selection and plot the points, as shown in the following code snippet:

    lena1 = lena.copy() 
    xindices = get_indices(lena.shape[0])
    yindices = get_indices(lena.shape[1])
    lena1[xindices, yindices] = 0
    plt.subplot(211)
    plt.imshow(lena1)
  3. Select array values between a quarter and three-quarters of the maximum value, and set them to 0, as shown in the...

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