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

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.

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