stack(*args, **kwargs)
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.
The function is applied to both the _data and the _mask, if any.
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 :
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them