nums.numpy.hstack

nums.numpy.hstack(tup)[source]

Stack arrays in sequence horizontally (column wise).

This docstring was copied from numpy.hstack.

Some inconsistencies with the NumS version may exist.

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.

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 BlockArray) – The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
Returns: stacked – The array formed by stacking the given arrays.
Return type: BlockArray

See also

concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
block: Assemble an nd-array from nested lists of blocks.
vstack: Stack arrays in sequence vertically (row wise).
dstack: Stack arrays in sequence depth wise (along third axis).
column_stack: Stack 1-D arrays as columns into a 2-D array.
hsplit: Split an array into multiple sub-arrays horizontally (column-wise).

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array((1,2,3))  
>>> b = nps.array((2,3,4))  
>>> nps.hstack((a,b)).get()  
array([1, 2, 3, 2, 3, 4])
>>> a = nps.array([[1],[2],[3]])  
>>> b = nps.array([[2],[3],[4]])  
>>> nps.hstack((a,b)).get()  
array([[1, 2],
       [2, 3],
       [3, 4]])

nums.numpy.atleast_3d

nums.numpy.vstack