If I start with a 3x4 array, and concatenate a 3x1 array, with axis 1, I get a 3x5 array:
In [911]: x = np.arange(12).reshape(3,4)
In [912]: np.concatenate([x,x[:,-1:]], axis=1)
Out[912]:
array([[ 0, 1, 2, 3, 3],
[ 4, 5, 6, 7, 7],
[ 8, 9, 10, 11, 11]])
In [913]: x.shape,x[:,-1:].shape
Out[913]: ((3, 4), (3, 1))
Note that both inputs to concatenate have 2 dimensions.
Omit the :, and x[:,-1] is (3,) shape - it is 1d, and hence the error:
In [914]: np.concatenate([x,x[:,-1]], axis=1)
...
ValueError: all the input arrays must have same number of dimensions
The code for np.append is (in this case where axis is specified)
return concatenate((arr, values), axis=axis)
So with a slight change of syntax append works. Instead of a list it takes 2 arguments. It imitates the list append is syntax, but should not be confused with that list method.
In [916]: np.append(x, x[:,-1:], axis=1)
Out[916]:
array([[ 0, 1, 2, 3, 3],
[ 4, 5, 6, 7, 7],
[ 8, 9, 10, 11, 11]])
np.hstack first makes sure all inputs are atleast_1d, and then does concatenate:
return np.concatenate([np.atleast_1d(a) for a in arrs], 1)
So it requires the same x[:,-1:] input. Essentially the same action.
np.column_stack also does a concatenate on axis 1. But first it passes 1d inputs through
array(arr, copy=False, subok=True, ndmin=2).T
This is a general way of turning that (3,) array into a (3,1) array.
In [922]: np.array(x[:,-1], copy=False, subok=True, ndmin=2).T
Out[922]:
array([[ 3],
[ 7],
[11]])
In [923]: np.column_stack([x,x[:,-1]])
Out[923]:
array([[ 0, 1, 2, 3, 3],
[ 4, 5, 6, 7, 7],
[ 8, 9, 10, 11, 11]])
All these 'stacks' can be convenient, but in the long run, it's important to understand dimensions and the base np.concatenate. Also know how to look up the code for functions like this. I use the ipython ?? magic a lot.
And in time tests, the np.concatenate is noticeably faster - with a small array like this the extra layers of function calls makes a big time difference.