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, ifaxis=0
(the default) it will be the first dimension and ifaxis=-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 ofstack
, 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_