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Scientific Computing with Python - Second Edition

You're reading from  Scientific Computing with Python - Second Edition

Product type Book
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Pages 392 pages
Edition 2nd Edition
Languages
Authors (3):
Claus Führer Claus Führer
Profile icon Claus Führer
Jan Erik Solem Jan Erik Solem
Olivier Verdier Olivier Verdier
View More author details

Table of Contents (23) Chapters

Preface Getting Started Variables and Basic Types Container Types Linear Algebra - Arrays Advanced Array Concepts Plotting Functions Classes Iterating Series and Dataframes - Working with Pandas Communication by a Graphical User Interface Error and Exception Handling Namespaces, Scopes, and Modules Input and Output Testing Symbolic Computations - SymPy Interacting with the Operating System Python for Parallel Computing Comprehensive Examples About Packt Other Books You May Enjoy References

5.6.3 Sparse matrix methods

There are methods to convert one sparse type into another or into an array:

AS.toarray # converts sparse formats to a numpy array 
AS.tocsr
AS.tocsc
AS.tolil

The type of a sparse matrix can be inspected by the methods issparse, isspmatrix_lil, isspmatrix_csr, and isspmatrix_csc.

Elementwise operations +, *, /, and ** on sparse matrices are defined as for NumPy arrays. Regardless of the sparse matrix format of the operands, the result is always csr_matrix. Applying elementwise operating functions to sparse matrices requires first transforming them to either CSR or CSC format and applying the functions to their data attribute, as demonstrated by the next example.

The elementwise sine of a sparse matrix can be defined by an operation on its data attribute:

import scipy.sparse as sp
def sparse_sin(A):
    if not (sp.isspmatrix_csr(A) or sp.isspmatrix_csc(A)):
        A = A.tocsr()
    A.data = sin(A.data...
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