# numpy.hstack¶

`numpy.``hstack`(tup)[source]

Stack arrays in sequence horizontally (column wise).

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by `hsplit`.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions `concatenate`, `stack` and `block` provide more general stacking and concatenation operations.

Parameters: tup : sequence of ndarrays The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length. stacked : ndarray The array formed by stacking the given arrays.

`stack`
Join a sequence of arrays along a new axis.
`vstack`
Stack arrays in sequence vertically (row wise).
`dstack`
Stack arrays in sequence depth wise (along third axis).
`concatenate`
Join a sequence of arrays along an existing axis.
`hsplit`
Split array along second axis.
`block`
Assemble arrays from blocks.

Examples

```>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([,,])
>>> b = np.array([,,])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
```

numpy.dstack

numpy.vstack