Python for Computational Linear Algebra

Python packages

• numpy (numerical linear algebra)
• sympy (symbolic & computational linear algebra)

Basic method

sympy library

Method	Usage
.rref()	Gauss-Jordan eliminations
.rank()	Matrix rank
.col_insert()	Insert columns
.row_insert()	Insert rows
.det()	Matrix determinant
.eigenvals()	Matrix eigenvalues
.eigenvects()	Matrix eigenvactors
.shape()	Matrix shape
@	Matrix multiplication
.T	Transpose matrix
**(-1)	Inverse matrix

numpy.linalg library

Method	Usage
.matrix_power(a,n)	Matrix power
.eig(a)	eigenvalues & eigenvetctors
.eigvals(a)	eigenvalues only
.det(a)	Matrix determonant
.matrix_rank()	Matrix rank
.solve(a, b)	Solve a linear matrix equation, or system of linear scalar equations
.inv(a)	the inverse of a matrix
.norm()	Matrix/vector norm