We talked about the usual indexing. But besides this there is also negative indexing. Negative indexing means starting from the end, i.e. index -1 refers to the last element, index -2 refers to the penultimate element, and so on.



import numpy as np

arr = np.array([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]])

print(arr[-1 , -1])

The example shows how we can get a value of **10** from a given two-dimensional array using negative indexing.
The first index is responsible for which row we choose (-1 is the last). Another index corresponds to this element, which we select in this row (-1 is the last one). As a result, we got a value of 10. Run the code above and you will see it.

In this section we will get acquainted with what the NumPy library is, as well as learn how to create an array.

In this section we will get acquainted with arrays of different dimensions, and understand the difference between them.

In this section we should recall what slices are and learn how to make them for arrays of different dimensions. We shall also learn to refer to elements in an array using their indexes.

In this section we will learn how to reshape arrays and also how to concatenate an array. Moreover we will learn how to sort an array. Also we will learn about such an interesting method that is often used for arrays, namely: copy().