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?
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.
numpy.vstack: stack arrays in sequence vertically (row wise).Equivalent to
np.concatenate(tup, axis=0) example see: https://docs.scipy.org/doc/numpy/reference/generated/numpy.vstack.html
numpy.hstack: Stack arrays in sequence horizontally (column wise).Equivalent to
np.concatenate(tup, axis=1), except for 1-D arrays where it concatenates along the first axis. example see:
append is a function for python’s built-in data structure
list. Each time you add an element to the list. Obviously, To add multiple elements, you will use
extend. Simply put, numpy’s functions are much more powerful.
suppose gray.shape = (n0,n1)
np.vstack((gray,gray,gray)) will have shape (n0*3, n1), you can also do it by
np.hstack((gray,gray,gray)) will have shape (n0, n1*3), you can also do it by
np.dstack((gray,gray,gray)) will have shape (n0, n1,3).
In IPython you can look at the source code of a function by typing its name followed by
??. Taking a look at
hstack we can see that it’s actually just a wrapper around
concatenate (similarly with
np.hstack?? def hstack(tup): ... arrs = [atleast_1d(_m) for _m in tup] # As a special case, dimension 0 of 1-dimensional arrays is "horizontal" if arrs.ndim == 1: return _nx.concatenate(arrs, 0) else: return _nx.concatenate(arrs, 1)
So I guess just use whichever one has the most logical sounding name to you.
If you have two matrices, you’re good to go with just
If you’re stacking a matrice and a vector,
hstack becomes tricky to use, so
column_stack is a better option:
If you’re stacking two vectors, you’ve got three options:
concatenate in its raw form is useful for 3D and above, see
my article Numpy Illustrated for details.