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).

  • 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).


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

Return type


See also


Join a sequence of arrays along an existing axis.


Assemble an nd-array from nested lists of blocks.


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


>>> 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.