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. But for arrays representing matrices...