Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
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 second axis,
except 1-D arrays which can be any length.
The array formed by stacking the given arrays.
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 vertically (row 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 horizontally (column-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([,,])