# NumPy - split() function

The NumPy split() function splits an array into multiple sub-arrays as views into ary.

### Syntax

```numpy.split(ary, indices_or_sections, axis=0)
```

### Parameters

 `ary` `Required. `Specify the array (ndarray) to be divided into sub-arrays. `indices_or_sections` `Required. ` Specify indices_or_sections as int or 1-D array. If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If such a split is not possible, an error is raised. If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, [2, 3] would, for axis=0, result in: ary[:2] ary[2:3] ary[3:] If an index exceeds the dimension of the array along axis, an empty sub-array is returned correspondingly. `axis` `Optional. `Specify the axis along which to split. Default is 0.

### Return Value

Returns a list of sub-arrays as views into ary.

### Example:

In the example below, split() function is used to split a given array.

```import numpy as np

Arr = np.array([[10, 20, 30],
[30, 40, 60],
[70, 80, 90]])

#splitting the array
Arr1 = np.split(Arr, 3)

#displaying results
print("Arr is:")
print(Arr)
print("\nArr1 is:")
print(Arr1)
```

The output of the above code will be:

```Arr is:
[[10 20 30]
[30 40 60]
[70 80 90]]

Arr1 is:
[array([[10, 20, 30]]), array([[30, 40, 60]]), array([[70, 80, 90]])]
```

### Example: indices_or_sections as 1-D array

When indices_or_sections is passed as 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, [2, 3] would, for axis=0, result in: array[:2], array[2:3], array[3:].

```import numpy as np

Arr = np.array([[10, 20, 30],
[30, 40, 60],
[70, 80, 90],
[100, 200, 300]])

#splitting the array
Arr1 = np.split(Arr, [2,3])

#displaying results
print("Arr is:")
print(Arr)
print("\nArr1 is:")
print(Arr1)
```

The output of the above code will be:

```Arr is:
[[ 10  20  30]
[ 30  40  60]
[ 70  80  90]
[100 200 300]]

Arr1 is:
[array([[10, 20, 30],
[30, 40, 60]]), array([[70, 80, 90]]), array([[100, 200, 300]])]
```

❮ NumPy - Functions

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