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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
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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

4.8.2 Array functions

There are a number of functions acting on arrays that do not act componentwise. Examples of such functions are max, min, and sum. These functions may operate on the entire matrix, row-wise, or column-wise. When no argument is provided, they act on the entire matrix.

Suppose:

The function sum acting on that matrix returns a scalar:

sum(A) # 36

The command has an optional parameter, axis. It allows us to choose along which axis to perform the operation. For instance, if the axis is , it means that the sum should be computed along the first axis. The sum along axis  of an array of shape  will be a vector of length .

Suppose we compute the sum of A along the axis :

sum(A, axis=0) # array([ 6, 8, 10, 12])

This amounts to computing the sum on the columns:

The result is a vector:

Now suppose we compute the sum along axis 1:

A.sum(axis=1) # array([10, 26])

This amounts to computing...

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