# jax.numpy.vstackÂ¶

jax.numpy.vstack(tup)[source]Â¶

Stack arrays in sequence vertically (row wise).

LAX-backend implementation of vstack(). Original docstring below.

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) â€“ The arrays must have the same shape along all but the first axis. 1-D arrays must have the same length.

Returns

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

Return type

ndarray

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]])