Another equally important operation in array manipulation is **joining** arrays. Joining arrays involves combining multiple arrays into a single array that includes all the elements from each of the original arrays. 

We perform this concatenation along the specified axes:
 - if `axis = 0` (which is the default value), this implies concatenating the arrays by rows;
 - if `axis = 1`, this means concatenating 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)

Concatenate 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)

Concatenate two 2-D arrays along rows (default `axis=0`):

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)

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().

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NumPy in a Nutshell

Joining

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