/NumPy 1.17

# numpy.ma.dstack

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

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

This is equivalent to concatenation along the third axis after 2-D arrays of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by `dsplit`.

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 arrays` The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape. `stacked : ndarray` The array formed by stacking the given arrays, will be at least 3-D.

`stack`
Join a sequence of arrays along a new axis.
`vstack`
Stack along first axis.
`hstack`
Stack along second axis.
`concatenate`
Join a sequence of arrays along an existing axis.
`dsplit`
Split array along third axis.

#### Notes

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

#### Examples

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