To concatenate two arrays with NumPy:

- Import
**numpy**. - Put two arrays in a list.
- Call
**numpy.concatenate()**on the list of arrays.

For instance:

import numpy as np A = np.array([1, 2]) B = np.array([3, 4]) C = np.concatenate([A, B]) print(C)

Output:

[1 2 3 4]

This is a quick answer.

To learn more ways to concatenate arrays and about their efficiency, please, stick around.

## 4 Ways to Concatenate 1D NumPy Arrays

There are four built-in ways to concatenate arrays in NumPy.

Before introducing these, it is important you understand that all these approaches use the **numpy.concatenate() **under the hood.

You probably are going to use one of these four. But it is still worth understanding that other options exist. Furthermore, it is insightful to see how these perform against one another.

### 1. numpy.r_

The **numpy.r_** concatenates slice objects along the first axis. It offers you to build up arrays quickly.

One way to use **r_** is to concatenate two 1D arrays.

For instance:

import numpy as np A = np.array([1, 2]) B = np.array([3, 4]) C = np.r_[A, B] print(C)

Output:

[1 2 3 4]

### 2. numpy.stack.reshape

The **numpy.stack()** function joins a collection of arrays along a new axis.

When you have joined two arrays using **stack()** you can call the **reshape(-1)** function to flatten the array of arrays.

For instance:

import numpy as np A = np.array([1, 2]) B = np.array([3, 4]) C = np.stack([A, B]).reshape(-1) print(C)

Output:

[1 2 3 4]

### 3. numpy.hstack

The **numpy.hstack()** function stacks a sequence column-wise. In other words, the function concatenates the arrays:

- Along the second axis in general.
- Along the first axis on 1D arrays.

Thus, you can use this function to concatenate two arrays.

For instance:

import numpy as np A = np.array([1, 2]) B = np.array([3, 4]) C = np.hstack([A, B]) print(C)

Output:

[1 2 3 4]

### 4. numpy.concatenate

The** numpy.concatenate()** function merges two arrays together, forming a new array with all the elements from the original arrays.

For instance:

import numpy as np A = np.array([1, 2]) B = np.array([3, 4]) C = np.concatenate([A, B]) print(C)

Output:

[1 2 3 4]

## Performance Comparison

Let’s see how each of the concatenation approaches perform against one another.

import numpy as np import perfplot perfplot.show( setup=lambda n: np.random.rand(n), kernels=[ lambda A: np.r_[A, A], lambda A: np.stack([A, A]).reshape(-1), lambda A: np.hstack([A, A]), lambda A: np.concatenate([A, A]), ], labels=["np.r_", "np.stack.reshape", "np.hstack", "np.concatenate"], n_range=[2 ** i for i in range(20)], xlabel="len(A)", )

Output:

As you can see, the **np.concatenate()** out-performs the other approaches when the array sizes are small. However, the differences get smaller and smaller as the array size increases.

## Conclusion

Today you learned how to concatenate 1D NumPy arrays.

To recap, use the** numpy.concatenate() **function to join two arrays together, by providing the arrays as a list to the function.

Also, there are 3 alternative approaches:

**numpy.r_****numpy.stack.reshape****numpy.hstack**

Notice that all these approaches use the **numpy.concatenate()** behind the scenes.

Thanks for reading.

Happy coding!

## Further Reading

Best Python Courses for Data Science