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.

What does numpy hsplit() do?

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.

1. Split 1d numpy array horizontally

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.

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.

2. Split 2d numpy array horizontally

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

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

Using numpy split() with axis=1

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