# numpy.ma.hstack¶

ma.hstack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>

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
tupsequence of ndarrays

The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.

Returns
stackedndarray

The array formed by stacking the given arrays.

See also

`concatenate`

Join a sequence of arrays along an existing axis.

`stack`

Join a sequence of arrays along a new axis.

`block`

Assemble an nd-array from nested lists of blocks.

`vstack`

Stack arrays in sequence vertically (row wise).

`dstack`

Stack arrays in sequence depth wise (along third axis).

`column_stack`

Stack 1-D arrays as columns into a 2-D array.

`hsplit`

Split an array into multiple sub-arrays horizontally (column-wise).

Notes

The function is applied to both the _data and the _mask, if any.

Examples

```>>> a = np.array((1,2,3))
>>> b = np.array((4,5,6))
>>> np.hstack((a,b))
array([1, 2, 3, 4, 5, 6])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[4],[5],[6]])
>>> np.hstack((a,b))
array([[1, 4],
[2, 5],
[3, 6]])
```