As mentioned in Chapter 2, Working with the NumPy Array As a First Step to SciPy, SciPy depends on NumPy's main object's ndarray data structure. You can look at one-dimensional arrays as vectors and vice versa (oriented points in an n-dimensional space). Consequently, a vector can be created via Numpy as follows:
>>> import numpy >>> vectorA = numpy.array([1,2,3,4,5,6,7]) >>> vectorA
The output is shown as follows:
array([1, 2, 3, 4, 5, 6, 7])
We can also use already defined arrays to create a new candidate. Some examples were presented in the previous chapter. Here we can reverse the already created vector and assign it to a new one:
>>> vectorB = vectorA[::-1].copy() >>> vectorB
The output is shown as follows:
array([7, 6, 5, 4, 3, 2, 1])
Notice that in this example, we have to make a copy of the reverse of the elements of vectorA and assign it to vectorB. This way, by changing elements of vectorB, the elements of vectorA remain...
