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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

5.6.2 Generating sparse matrices

The NumPy commands eye, identity, diag, and rand have their sparse counterparts. They take an additional argument; it specifies the sparse matrix format of the resulting matrix.

The following commands generate the identity matrix but in different sparse matrix formats:

import scipy.sparse as sp
sp.eye(20,20,format = 'lil') 
sp.spdiags(ones((20,)),0,20,20, format = 'csr') 
sp.identity(20,format ='csc')

The command sp.rand takes an additional argument describing the density of the generated random matrix. A dense matrix has density 1 while a zero matrix has density 0:

import scipy.sparse as sp 
AS=sp.rand(20,200,density=0.1,format='csr')
AS.nnz # returns 400

There is no direct correspondence to the NumPy command zeroes. Matrices completely filled with zeros are generated by instantiating the corresponding type with the shape parameters as constructor parameters...

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