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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

4.2.1 Arrays as functions

Arrays may be considered from several different points of view. If you want to approach the concept from a mathematical point of view, you might benefit from understanding arrays through an analogy of functions of several variables. This view will later be taken again, when explaining the concept of broadcasting in Section 5.5: Broadcasting.

For instance, selecting a component of a given vector in may just be considered a function from the set of  to , where we define the set:

Here the set  has n elements. The Python function range generates .

Selecting an element of a given matrix, on the other hand, is a function of two parameters, taking its value in . Picking a particular element of an  matrix may thus be considered a function from  to .

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