# Horizontally split numpy array with hsplit()

In this tutorial, we will look at the numpy hsplit() function and its usage with the help of some examples.

The numpy hsplit() function is used to split a numpy array into multiple sub-arrays horizontally (column-wise). Pass the input array and the number of sub-arrays as arguments. The following is the syntax:

```import numpy as np

# split array column-wise (horizontally)
sub_arrays = np.hsplit(arr, indices_or_sections)```

It returns a list of numpy arrays created from the split (the sub-arrays).

Let’s look at some examples of using the numpy hsplit() function.

A 1d numpy array can be thought of as an array with a single row and multiple columns. Let’s apply the `np.hsplit()` function to split a 1d array column-wise.

Highlighted programs for you

Flatiron School

Flatiron School

Data Science Bootcamp
Product Design UX/UI Bootcamp

University of Maryland Global Campus

University of Maryland Global Campus

Cloud Computing Systems Master's
Digital Forensics & Cyber Investigation Master's

Creighton University

Creighton University

Health Informatics Master's

```import numpy as np

# create a 1d numpy array
arr = np.array([1, 0, 1, 2, 2, 2])
# split the array into 2 subarrays horizontally
sub_arrays = np.hsplit(arr, 2)
# display the sub_arrays
print(sub_arrays)```

Output:

`[array([1, 0, 1]), array([2, 2, 2])]`

You can see that the resulting list has two sub-arrays (each 1d) created from splitting the input array horizontally.

Since the array is of length 6, we can also split it into 3 or 6 sub-arrays.

```# split the array into 3 subarrays horizontally
sub_arrays = np.hsplit(arr, 3)
# display the sub_arrays
print(sub_arrays)```

Output:

`[array([1, 0]), array([1, 2]), array([2, 2])]`

You can see that the returned list has 3 sub-arrays created from the column-wise split of the input array.

We can similarly split 2d numpy arrays. For example, let’s split a (3, 4) numpy array into two (3, 2) numpy arrays by splitting it horizontally (column-wise).

```# create a 2d numpy array
arr = np.array([[1, 2, 3, 4],
[2, 0, 0, 2],
[3, 1, 1, 0]])
# split the array into 2 subarrays horizontally
sub_arrays = np.hsplit(arr, 2)
# display the sub_arrays
sub_arrays```

Output:

```[array([[1, 2],
[2, 0],
[3, 1]]),
array([[3, 4],
[0, 2],
[1, 0]])]```

The resulting sub-arrays are of shape (3, 2).

Let’s now split the same input array into 4 sub-arrays of (3, 1) by splitting all the columns.

```# create a 2d numpy array
arr = np.array([[1, 2, 3, 4],
[2, 0, 0, 2],
[3, 1, 1, 0]])
# split the array into 4 subarrays horizontally
sub_arrays = np.hsplit(arr, 4)
# display the sub_arrays
sub_arrays```

Output:

```[array([,
,
]),
array([,
,
]),
array([,
,
]),
array([,
,
])]```

We get four (3, 1) sub-arrays.

Alternatively, you can perform a horizontal split with the numpy `split()` function. Just pass `axis=1` along with the input array and the number of sections to split it into.

Let’s split the above 2d array into two sub-arrays horizontally but using the numpy `split()` function this time.

```# create a 2d numpy array
arr = np.array([[1, 2, 3, 4],
[2, 0, 0, 2],
[3, 1, 1, 0]])
# split the array into 2 subarrays horizontally
sub_arrays = np.split(arr, 2, axis=1)
# display the sub_arrays
sub_arrays```

Output:

```[array([[1, 2],
[2, 0],
[3, 1]]),
array([[3, 4],
[0, 2],
[1, 0]])]```

For more on the numpy hsplit() function, refer to its documentation.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5

• 