xorbits.numpy.stack#

xorbits.numpy.stack(tensors, axis=0, out=None)[source]#

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

New in version 1.10.0(numpy).

Parameters

arrays (sequence of array_like (Not supported yet)) – Each array must have the same shape.
axis (int, optional) – The axis in the result array along which the input arrays are stacked.
out (ndarray, optional) – If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.
dtype (str or dtype (Not supported yet)) –
If provided, the destination array will have this dtype. Cannot be provided together with out.

New in version 1.24(numpy).
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional (Not supported yet)) –
Controls what kind of data casting may occur. Defaults to ‘same_kind’.

New in version 1.24(numpy).

Returns

stacked – The stacked array has one more dimension than the input arrays.

Return type

ndarray

See also

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.

Examples

>>> arrays = [np.random.randn(3, 4) for _ in range(10)]  
>>> np.stack(arrays, axis=0).shape  
(10, 3, 4)

>>> np.stack(arrays, axis=1).shape  
(3, 10, 4)

>>> np.stack(arrays, axis=2).shape  
(3, 4, 10)

>>> a = np.array([1, 2, 3])  
>>> b = np.array([4, 5, 6])  
>>> np.stack((a, b))  
array([[1, 2, 3],
       [4, 5, 6]])

>>> np.stack((a, b), axis=-1)  
array([[1, 4],
       [2, 5],
       [3, 6]])

tensorssequence of array_like: Each tensor must have the same shape.

This docstring was copied from numpy.