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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

4.6.3 Reshape

The method reshape gives a new view of the array, with a new shape, without copying the data:

v = array([0,1,2,3,4,5])
M = v.reshape(2,3)
shape(M) # returns (2,3)
M[0,0] = 10 # now v[0] is 10

The various effects of reshape on an array defined by arange(6) are given in Figure 4.2:

Figure 4.2: The various effects of reshape on an array

reshape does not create a new array. It rather gives a new view on the existing array. In the preceding example, changing one element of M would automatically result in a change in the corresponding element in v. When this behavior is not acceptable, you need to copy the data, as explained in Section 5.1: Array views and copies.

If you try to reshape an array with a shape that does not multiply to the original shape, an error is raised:

 ValueError: total size of new array must be unchanged.

Sometimes, it is convenient to specify only one shape parameter and let...

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