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Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
book

Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

by Robert Johansson
September 2024
Intermediate to advanced content levelIntermediate to advanced
501 pages
17h 6m
English
Apress
Content preview from Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2024
R. JohanssonNumerical Pythonhttps://doi.org/10.1007/979-8-8688-0413-7_10

10. Sparse Matrices and Graphs

Robert Johansson1  
(1)
Urayasu-shi, Chiba, Japan
 

We have gone through numerous examples of arrays and matrices that are essential in many aspects of numerical computing. And, we have represented arrays with the NumPy ndarray data structure, a heterogeneous representation that stores all the array elements it represents. This is often the most efficient way to represent an object, such as a vector, matrix, or higher-dimensional array. However, notable exceptions are matrices where most of the elements are zeros. Such matrices are known as sparse matrices ...

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