numpy.hstack

numpy.block

# numpy.vstack¶

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

Stack arrays in sequence vertically (row wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N). Rebuilds arrays divided by `vsplit`.

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 first axis. 1-D arrays must have the same length.

Returns
stackedndarray

The array formed by stacking the given arrays, will be at least 2-D.

`stack`

Join a sequence of arrays along a new axis.

`hstack`

Stack arrays in sequence horizontally (column wise).

`dstack`

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

`concatenate`

Join a sequence of arrays along an existing axis.

`vsplit`

Split array into a list of multiple sub-arrays vertically.

`block`

Assemble arrays from blocks.

Examples

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