We are back with another NumPy article. In this one, we will explore the np.array_split() function.

The array_split() function in NumPy allows us to split an array into sub-arrays of different dimensions.

Function Syntax

The syntax of the function is as shown in the code snippet below:

1	numpy.array_split(ary, indices_or_sections, axis=0)

Function Parameters

The function parameters are explained below:

ary – defines the input array.
indices_or_sections – determines the number of sections among which the array is split.
Axis – represents along which axis the collection is divided.

The function will return the array divided into different dimensions.

In the example below, we use the array_split method to split an array into two subarrays.

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# import numpy
import numpy as np
arr = np.arange(6).reshape(2,3)
print(np.array_split(arr, 2))

The code above should result in two subarrays as shown:

1	[array([[0, 1, 2]]), array([[3, 4, 5]])]

We can also perform the same operation on a two-dimensional array as illustrated in the code below:

1 2	arr = np.array([[1,2,3], [4,5,6], [7,8,9]]) print(np.array_split(arr, 3))

The above should return:

1	[array([[1, 2, 3]]), array([[4, 5, 6]]), array([[7, 8, 9]])]

Using this guide, you understood how to use NumPy’s array_split() function to split an array into different dimensions.

Thanks for reading & See you in the next one!!