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
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.
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.
The array formed by stacking the given arrays, will be at least 2-D.
Join a sequence of arrays along an existing axis.
Join a sequence of arrays along a new axis.
Assemble an nd-array from nested lists of blocks.
Stack arrays in sequence horizontally (column wise).
Stack arrays in sequence depth wise (along third axis).
Stack 1-D arrays as columns into a 2-D array.
Split an array into multiple sub-arrays vertically (row-wise).
>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
array([[1, 2, 3],
[4, 5, 6]])
>>> a = np.array([, , ])
>>> b = np.array([, , ])