Vector creation

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...

Tech Concepts

Programming languages

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Learning SciPy for Numerical and Scientific Computing Second Edition - Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition

Overview of this book

Vector creation

Learning SciPy for Numerical and Scientific Computing Second Edition - Second Edition

Learning SciPy for Numerical and Scientific Computing Second Edition

Overview of this book

Vector creation

Confirmation

Buy this book with your credits?

Submit Your Feedback

Create a Free Account To Continue Reading

Sign in to activate your 7-day free access