In this tutorial, **you’ll learn how to concatenate NumPy arrays in Python**. Knowing how to work with NumPy arrays is an important skill as you progress in data science in Python. Because NumPy arrays can be 1-dimensional or 2-dimensional, it’s important to understand the many different ways in which to join NumPy arrays.

By the end of this tutorial, you’ll have learned:

- How to use the
`concatenate()`

,`vstack()`

, and`hstack()`

functions - How to join or concatenate 1-dimensional and 2-dimensional arrays
- How to join NumPy arrays row-wise and column-wise

Table of Contents

## How to Join 1-dimensional NumPy Arrays Column-Wise

Let’s take a look at a simple example where we want to concatenate a one-dimensional NumPy array with another one-dimensional array. When joining 1-dimensional arrays, the elements are concatenated directly.

Let’s take a look at how this works:

```
# Concatenating 1-dimensional NumPy Arrays
import numpy as np
array1 = np.array([1,2,3,4])
array2 = np.array([5,6,7,8])
joined = np.concatenate((array1, array2))
print(joined)
# Returns: [1 2 3 4 5 6 7 8]
```

We can see that by passing in the two arrays that they were joined in the order they were listed. If we had switched the order in the `concatenate()`

function, the arrays would have returned `[5 6 7 8 1 2 3 4]`

.

## How to Join 1-dimensional NumPy Arrays Row-Wise

In order to join 1-dimensional arrays in NumPy row-wise, we need to use the `vstack()`

function. The `concatenate()`

function cannot work along the row-axis. However, the `vstack()`

function allows us to do this and returns a two-dimensional array.

Let’s take a look at an example:

```
# Concatenating 1-dimensional NumPy Arrays
import numpy as np
array1 = np.array([1,2,3,4])
array2 = np.array([5,6,7,8])
joined = np.vstack((array1, array2))
print(joined)
# Returns:
# [[1 2 3 4]
# [5 6 7 8]]
```

In the next section, you’ll learn how to concatenate two-dimensional arrays.

## How to Concatenate 2-dimensional NumPy Arrays Row-Wise

To join 2-dimensional NumPy arrays row-wise, we can also use the `concatenate()`

function. By row-wise, we mean that each array is added as though it were a row. By doing this, we concatenate along 0th axis.

The concatenate function assumes that the arrays are the same dimension, example for along the dimension along which it’s being concatenated.

Let’s take a look at an example:

```
# Concatenating 2-dimensional NumPy Arrays Row-Wise
import numpy as np
array1 = np.array([[1,2,3], [4,5,6]])
array2 = np.array([[11,22,33], [44,55,66]])
joined = np.concatenate((array1, array2))
print(joined)
# Returns:
# [[ 1 2 3]
# [ 4 5 6]
# [11 22 33]
# [44 55 66]]
```

In the next section, you’ll learn how to take on this task but join the arrays across the columns.

## How to Concatenate 2-dimensional NumPy Arrays Column-Wise

In order to join NumPy arrays column-wise, we can also use the `concatenate()`

function. In this case, however, we would use the `axis=1`

parameter, in order to specify that we want to join the arrays along the column axis.

Let’s take a look at an example:

```
# Concatenating 2-dimensional NumPy Arrays Column-Wise
import numpy as np
array1 = np.array([[1,2,3], [4,5,6]])
array2 = np.array([[11,22,33], [44,55,66]])
joined = np.concatenate((array1, array2), axis=1)
print(joined)
# Returns:
# [[ 1 2 3 11 22 33]
# [ 4 5 6 44 55 66]]
```

In the next section, you’ll learn a helper function to stack items row-wise.

## How to Stack 2-dimensional NumPy Arrays Row-Wise

NumPy also provides a helper function to join arrays row-wise. This function, `vstack()`

, calls the `concatenate()`

function and applies the default `axis=0`

argument. So, why would you use this function over concatenate? Readability.

The `vstack()`

function makes it clear that the function wants to stack the items vertically, meaning row-wise.

Let’s take a look at an example:

```
# Stacking NumPy Arrays Row-Wise
import numpy as np
array1 = np.array([[1,2,3], [4,5,6]])
array2 = np.array([[11,22,33], [44,55,66]])
joined = np.vstack((array1, array2))
print(joined)
# Returns:
# [[ 1 2 3]
# [ 4 5 6]
# [11 22 33]
# [44 55 66]]
```

In the next section, you’ll learn a helper function to stack items column-wise.

## How to Stack 2-dimensional NumPy Arrays Column-Wise

Similarly, NumPy provides a helper function to stack arrays column-wise. This function, `hstack()`

, also calls the `concatenate()`

function under the hood and applies the `axis=1`

argument. Similarly, this function allows your code to be more readable, making it immediately clear that the arrays should be joined column-wise.

Let’s take a look at an example:

```
# Stacking NumPy Arrays Column-Wise
import numpy as np
array1 = np.array([[1,2,3], [4,5,6]])
array2 = np.array([[11,22,33], [44,55,66]])
joined = np.hstack((array1, array2))
print(joined)
# Returns:
# [[ 1 2 3 11 22 33]
# [ 4 5 6 44 55 66]]
```

## Conclusion

In this tutorial, you learned how to join or concatenate NumPy arrays. You learned how to do this first for one-dimensional arrays, which can only be joined “column-wise”. Then, you learned how to use the `concatenate()`

function to join arrays along different axes. Finally, you learned about the helper functions, `vstack()`

and `hstack()`

, which call the `concatenate()`

function but help make your code more readable.

## Additional Resources

To learn more about related topics, check out the tutorials below: