# numpy.ma.row_stack¶

`numpy.ma.``row_stack`(tup) = <numpy.ma.extras._fromnxfunction_seq object>

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: tup : sequence of ndarrays The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length. stacked : ndarray 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.

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

numpy.ma.mr_

numpy.ma.vstack