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.

## Example #1

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

1 2 3 4 | # 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]])] |

## Example #2

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]])] |

## In Closing

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!!