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 arraysof shape(N,) have been reshaped to(1,N). Rebuilds arrays divided byvsplit.

This function makes most sense for arrays with up to 3 dimensions. Forinstance, for pixel-data with a height (first axis), width (second axis),and r/g/b channels (third axis). The functionsconcatenate,stack andblock 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.
Returns:	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.

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]])