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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

The broadcasting problem

When NumPy is given two arrays with different shapes and is asked to perform an operation that would require the two shapes to be the same, both arrays are broadcast to a common shape.

Suppose the two arrays have shapes  and . Broadcasting consists of the two steps:

  1. If the shape  is shorter than the shape , that is, len(s1) < len(s2), then ones are added on the left of the shape . This is reshaping.
  2. When the shapes have the same length, the first array is extended to match the shape s2 (if possible).

Suppose we want to add a vector of shape  to a matrix of shape . The vector needs to be broadcast. The first operation is reshaping; the shape of the vector is converted from (3, ) to (1, 3). The second operation is an extension; the shape is converted from (1, 3) to (4, 3).

For instance, suppose a vector of size n is to be broadcast to the shape (m, n):

  1.  is automatically reshaped to (1,...
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