nums.numpy.hstack

nums.numpy.hstack(tup)

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]])