The NumPy package offers arrays, which are container structures for manipulating vectors, matrices, or even higher order tensors in mathematics. In this section, we point out the similarities between arrays and lists. But arrays deserve a broader presentation, which will be given in Chapter 4, Linear Algebra – Arrays, and Chapter 5, Advanced Array Concepts.
Arrays are constructed from lists by the function array :
v = array([1.,2.,3.]) A = array([[1.,2.,3.],[4.,5.,6.]])
To access an element of a vector, we need one index, while an element of a matrix is addressed by two indexes:
v[2] # returns 3.0 A[1,2] # returns 6.0
At first glance, arrays are similar to lists, but be aware that they are different in a fundamental way, which can be explained by the following points:
- Access to array data corresponds to that of lists, using square brackets and slices. They may also be used to alter the array:
M = array([[1.,2.],[3.,4.]]) v = array([1., 2., 3.]) ...
