We discussed positive indexing, but there is also negative indexing. Negative indexing starts from the end, with index `-1` referring to the last element, index `-2` referring to the second-to-last 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])

This example illustrates how to retrieve a value of **10** from a given two-dimensional array using negative indexing. 

The first index determines the row we choose (`-1` refers to the last row), while the second index corresponds to the element we select within that row (`-1` is the last one). As a result, we obtain a value of `10`. Run the code above to verify it.

NumPy is one of the basic packages for scientific computing in Python. The 'NumPy in a Nutshell' course will introduce you to such a powerful tool as NumPy, which is convenient for working with arrays of different sizes. After completing this course, you will be able to easily work with matrices, using various functions. In addition, during the course, you will learn basic methods for working with arrays that simplify code writing.

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().

Negative Indexing

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