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stack(arrays, axis=0, out=None)

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.

versionadded

Parameters

arrays : sequence of array_like

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.

Returns

stacked : ndarray

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

Join a sequence of arrays along a new axis.

See Also

block

Assemble an nd-array from nested lists of blocks.

concatenate

Join a sequence of arrays along an existing axis.

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]])
See :

Back References

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numpy.hstack dask.array.routines.cov numpy.ma.extras.dstack numpy.column_stack numpy.dstack numpy.ma.extras.hstack numpy.block numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.vstack numpy.split numpy.ma.extras.vstack numpy.ma.extras.column_stack

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GitHub : /numpy/core/shape_base.py#357
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