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.
Each array must have the same shape.
The axis in the result array along which the input arrays are stacked.
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.
The stacked array has one more dimension than the input arrays.
Join a sequence of arrays along a new axis.
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.
>>> 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 :
The following pages refer to to this document either explicitly or contain code examples using this.
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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