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# NumPy | array_split method

schedule Aug 12, 2023
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NumPy's `array_split(~)` method divides up the input array as per the specified parameters.

NOTE

The parameters and the functionality of the two `array_split(~)` and `split(~)` are the exact same. The key difference is that `array_split(~)` allows for unequal division given an int for the 2nd parameter, while the `split(~)` method would throw an error.

For `array_split(~)`, the last partition would hold less data to accommodate he unequal division.

# Parameters

1. `a` | `array-like`

The input array that you want to split.

2. `indices_or_sections` | `int`

If an int, n, is given, then the input array will be split up into n equal arrays along the specified axis. To be more precise about where to split up the arrays, provide a 1D array of sorted integers instead.

3. `axis` | `None` or `int` | `optional`

The axis along which to perform the split. By default, `axis=0`.

# Return value

A list containing the split up NumPy arrays.

# Examples

## Basic usage

``` a = np.array([4,5,6,7,8,9])np.array_split(a, 2) [array([4, 5, 6]), array([7, 8, 9])] ```

When the split is not even, no error is thrown:

``` a = np.array([4,5,6,7,8])np.array_split(a, 2) [array([4, 5, 6]), array([7, 8])] ```

## Splitting via slicing

``` a = np.array([4,5,6,7,8,9])np.array_split(a, (2,3)) [array([4, 5]), array([6]), array([7, 8, 9])] ```

Here, we're slicing like follows:

``` a[:2]a[2:3]a[3:] ```

## Splitting 2D arrays

Consider the following 2D array:

``` a = np.array([[1,2,3,4],[5,6,7,8]])a array([[1, 2, 3, 4], [5, 6, 7, 8]]) ```

### Chop horizontally

To split `a` by rows:

``` np.array_split(a, 2, axis=0) [array([[1, 2, 3, 4]]), array([[5, 6, 7, 8]])] ```

### Chop vertically

To split `a` by columns:

``` np.array_split(a, 2, axis=1) [array([[1, 2], [5, 6]]), array([[3, 4], [7, 8]])] ```

### Slicing

To split `a` by columns via slicing:

``` np.array_split(a, (2,3), axis=1) [array([[1, 2], [5, 6]]), array([[3], [7]]), array([[4], [8]])] ```

Here, the columns we obtain are:

``` first two columnsthird columnfourth column ```
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