# numpy.unstack#

numpy.unstack(x, /, *, axis=0)[source]#

Split an array into a sequence of arrays along the given axis.

The `axis` parameter specifies the dimension along which the array will be split. For example, if `axis=0` (the default) it will be the first dimension and if `axis=-1` it will be the last dimension.

The result is a tuple of arrays split along `axis`.

New in version 2.1.0.

Parameters:
xndarray

The array to be unstacked.

axisint, optional

Axis along which the array will be split. Default: `0`.

Returns:
unstackedtuple of ndarrays

The unstacked arrays.

`stack`

Join a sequence of arrays along a new axis.

`concatenate`

Join a sequence of arrays along an existing axis.

`block`

Assemble an nd-array from nested lists of blocks.

`split`

Split array into a list of multiple sub-arrays of equal size.

Notes

`unstack` serves as the reverse operation of `stack`, i.e., `stack(unstack(x, axis=axis), axis=axis) == x`.

This function is equivalent to `tuple(np.moveaxis(x, axis, 0))`, since iterating on an array iterates along the first axis.

Examples

```>>> arr = np.arange(24).reshape((2, 3, 4))
>>> np.unstack(arr)
(array([[ 0,  1,  2,  3],
[ 4,  5,  6,  7],
[ 8,  9, 10, 11]]),
array([[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]))
>>> np.unstack(arr, axis=1)
(array([[ 0,  1,  2,  3],
[12, 13, 14, 15]]),
array([[ 4,  5,  6,  7],
[16, 17, 18, 19]]),
array([[ 8,  9, 10, 11],
[20, 21, 22, 23]]))
>>> arr2 = np.stack(np.unstack(arr, axis=1), axis=1)
>>> arr2.shape
(2, 3, 4)
>>> np.all(arr == arr2)
np.True_
```