Indexing and slicing are similar to the corresponding operations for lists. The main difference is that there may be several indexes or slices when the array is a matrix. The subject will be covered in depth in Section 4.4.1: Basic array slicing; here, we just give some illustrative examples of indexing and slicing:
v = array([1., 2., 3]) M = array([[1., 2],[3., 4]]) v[0] # works as for lists v[1:] # array([2., 3.]) M[0, 0] # 1. M[1:] # returns the matrix array([[3., 4]]) M[1] # returns the vector array([3., 4.]) # access v[0] # 1. v[0] = 10 # slices v[:2] # array([10., 2.]) v[:2] = [0, 1] # now v == array([0., 1., 3.]) v[:2] = [1, 2, 3] # error!
As arrays are the basic datatype for all tasks in computational linear algebra, we now present in this overview section some examples, the dot product and the solution of linear equation systems.
