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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
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Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Array indexing


We have already seen that one may index arrays by combinations of slices and integers, this is the basic slicing technique. There are, however, many more possibilities, which allow for a variety of ways to access and modify array elements.

Indexing with Boolean arrays

It is often useful to access and modify only parts of an array, depending on its value. For instance, one might want to access all the positive elements of an array. This turns out to be possible using Boolean arrays, which act like masks to select only some elements of an array. The result of such an indexing is always a vector. For instance, consider the following example:

B = array([[True, False],
           [False, True]])
M = array([[2, 3],
           [1, 4]])
M[B] # array([2,4]), a vector

In fact, the M[B] call is equivalent to M.flatten()[B]. One may then replace the resulting vector by another vector. For instance, one may replace all the elements by zero (refer to section Broadcasting...

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