Asked By – Stefan Sullivan
Simple question: what is the advantage of each of these methods. It seems that given the right parameters (and ndarray shapes) they all work seemingly equivalently. Do some work in place? Have better performance? Which functions should I use when?
Now we will see solution for issue: When should I use hstack/vstack vs append vs concatenate vs column_stack?
All the functions are written in Python except
np.concatenate. With an IPython shell you just use
If not, here’s a summary of their code:
vstack concatenate([atleast_2d(_m) for _m in tup], 0) i.e. turn all inputs in to 2d (or more) and concatenate on first hstack concatenate([atleast_1d(_m) for _m in tup], axis=<0 or 1>) colstack transform arrays with (if needed) array(arr, copy=False, subok=True, ndmin=2).T append concatenate((asarray(arr), values), axis=axis)
In other words, they all work by tweaking the dimensions of the input arrays, and then concatenating on the right axis. They are just convenience functions.
arrays = [asanyarray(arr) for arr in arrays] shapes = set(arr.shape for arr in arrays) result_ndim = arrays.ndim + 1 axis = normalize_axis_index(axis, result_ndim) sl = (slice(None),) * axis + (_nx.newaxis,) expanded_arrays = [arr[sl] for arr in arrays] concatenate(expanded_arrays, axis=axis, out=out)
That is, it expands the dims of all inputs (a bit like
np.expand_dims), and then concatenates. With
axis=0, the effect is the same as
hstack documentation now adds:
blockprovide more general stacking and concatenation operations.
np.block is also new. It, in effect, recursively concatenates along the nested lists.
This question is answered By – hpaulj
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