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.

See also

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_