numpy.stack#

numpy.stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind')[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.

Parameters:

arrayssequence of ndarrays: Each array must have the same shape. In the case of a single ndarray array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.
axisint, optional: The axis in the result array along which the input arrays are stacked.
outndarray, 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.
dtypestr or dtype: If provided, the destination array will have this dtype. Cannot be provided together with out.

New in version 1.24.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional: Controls what kind of data casting may occur. Defaults to ‘same_kind’.

New in version 1.24.

Returns:

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

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.
unstack: Split an array into a tuple of sub-arrays along an axis.

Examples

>>> import numpy as np
>>> rng = np.random.default_rng()
>>> arrays = [rng.normal(size=(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]])