xorbits.numpy.hstack#

xorbits.numpy.hstack(tup)[source]#

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 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 ndarrays) – The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
dtype (str or dtype (Not supported yet)) – If provided, the destination array will have this dtype. Cannot be provided together with out.
versionadded: (..) – 1.24(numpy):
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional (Not supported yet)) – Controls what kind of data casting may occur. Defaults to ‘same_kind’.
versionadded: – 1.24(numpy):

Returns

stacked – The array formed by stacking the given arrays.

Return type

ndarray

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

>>> a = np.array((1,2,3))  
>>> b = np.array((4,5,6))  
>>> np.hstack((a,b))  
array([1, 2, 3, 4, 5, 6])
>>> a = np.array([[1],[2],[3]])  
>>> b = np.array([[4],[5],[6]])  
>>> np.hstack((a,b))  
array([[1, 4],
       [2, 5],
       [3, 6]])

This docstring was copied from numpy.