Another equally important function in working with arrays is array joining. Joining arrays means making one common array from several arrays, which will contain all the elements from each array. We concatenate arrays  along the axes.
 - If the `axis = 0` (this is the default value) then this means we concatenate the arrays by rows.
 - If the `axis = 1` then this means we concatenate the arrays by columns.

import numpy as np

array_1 = np.array([54, 6, 23, 1, 6])
array_2 = np.array([12, 67, 94, 2, 8 ])

array = np.concatenate((array_1, array_2))

print(array)

Join two 2-D arrays along columns (axis=1):

import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2), axis=1)

print(array)

Join two 2-D arrays along rows (axis=0, as default):

import numpy as np

array_1 = np.array([[0, 1, 2], [3, 4, 5]])
array_2 = np.array([[6, 7, 8], [9, 10, 11]])

array = np.concatenate((array_1, array_2))

print(array)

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().