# numpy.dstack¶

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

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

Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by `dsplit`. This is a simple way to stack 2D arrays (images) into a single 3D array for processing.

This function continues to be supported for backward compatibility, but you should prefer `np.concatenate` or `np.stack`. The `np.stack` function was added in NumPy 1.10.

Parameters: tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis. stacked : ndarray The array formed by stacking the given arrays.

`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

Equivalent to `np.concatenate(tup, axis=2)` if tup contains arrays that are at least 3-dimensional.

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]]])
```

#### Previous topic

numpy.column_stack

numpy.hstack