### Question :

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?

##
Answer #1:

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.

And newer `np.stack`

:

```
arrays = [asanyarray(arr) for arr in arrays]
shapes = set(arr.shape for arr in arrays)
result_ndim = arrays[0].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 `np.array`

.

`hstack`

documentation now adds:

The functions

`concatenate`

,`stack`

and

`block`

provide more general stacking and concatenation operations.

`np.block`

is also new. It, in effect, recursively concatenates along the nested lists.

##
Answer #2:

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:

https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html

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.

example:

suppose gray.shape = (n0,n1)

`np.vstack((gray,gray,gray))`

will have shape (n0*3, n1), you can also do it by `np.concatenate((gray,gray,gray),axis=0)`

`np.hstack((gray,gray,gray))`

will have shape (n0, n1*3), you can also do it by `np.concatenate((gray,gray,gray),axis=1)`

`np.dstack((gray,gray,gray))`

will have shape (n0, n1,3).

##
Answer #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 `vstack`

and `column_stack`

):

```
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[0].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.

##
Answer #4:

If you have two matrices, you’re good to go with just `hstack`

and `vstack`

:

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:

And `concatenate`

in its raw form is useful for 3D and above, see

my article Numpy Illustrated for details.