/NumPy 1.17

# numpy.ma.row_stack

`numpy.ma.row_stack(*args, **kwargs) = <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.

See also

`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([, , ])
>>> b = np.array([, , ])
>>> np.vstack((a,b))
array([,
,
,
,
,
])
```

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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.ma.row_stack.html