#### Overview of this book

Learning SciPy for Numerical and Scientific Computing Second Edition
Credits
www.PacktPub.com
Preface
Free Chapter
Introduction to SciPy
Working with the NumPy Array As a First Step to SciPy
SciPy for Linear Algebra
SciPy for Numerical Analysis
SciPy for Signal Processing
SciPy for Data Mining
SciPy for Computational Geometry
Interaction with Other Languages
Index

## 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` remain...