# NumPy array join | Comprehensive tutorials 10 | Better4Code

**What is NumPy array join?** Numpy is a widely-used Python library for scientific computing and data analysis. When working with data, it’s common to need to combine two or more numpy arrays into a single array. In this article, we will be discussing the numpy array join function and its importance in data analysis.

The numpy array join function is used to combine two or more numpy arrays into a single array. There are several ways to join numpy arrays, including concatenation, stacking, and appending.

**Concatenation** is the process of joining two or more arrays along an existing axis.

The concatenate function in numpy is used to concatenate arrays. For example, if you have two numpy arrays **a** and **b** with the same number of columns, you can concatenate them horizontally using the following code:

import numpy as np a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6], [7, 8]]) c = np.concatenate((a, b), axis=1) print(c)

The output of this code will be:

[[1 2 5 6] [3 4 7 8]]

As you can see, the concatenate function has joined the two arrays horizontally along the columns axis.

Stacking is the process of joining two or more arrays along a new axis. The stack function in numpy is used to stack arrays. For example, if you have two numpy arrays **a** and **b** with the same shape, you can stack them vertically using the following code:

import numpy as np a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6], [7, 8]]) c = np.stack((a, b), axis=0) print(c)

The output of this code will be:

[[[1 2] [3 4]] [[5 6] [7 8]]]

As you can see, the stack function has created a new axis and stacked the two arrays along it.

Appending is the process of adding new elements to an existing array. The append function in numpy is used to append elements to an array. For example, if you have a numpy array a and you want to append a new row to it, you can use the following code:

import numpy as np a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6]]) c = np.append(a, b, axis=0) print(c)

The output of this code will be:

[[1 2] [3 4] [5 6]]

As you can see, the append function has added a new row to the existing array.

**In conclusion**, joining numpy arrays is a crucial concept in data analysis and scientific computing. It allows you to combine data from multiple sources and perform a wide range of data analysis operations. Understanding how to join numpy arrays using concatenation, stacking, and appending is essential for performing advanced data analysis operations in Python.