numpy.ma.hstack¶

numpy.ma.hstack(tup) = <numpy.ma.extras._fromnxfunction_seq object>¶

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:

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.
Returns:	stacked : ndarray The array formed by stacking the given arrays.

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.

Returns:

stacked : ndarray

The array formed by stacking the given arrays.

See also

stack: Join a sequence of arrays along a new axis.
vstack: Stack arrays in sequence vertically (row wise).
dstack: Stack arrays in sequence depth wise (along third axis).
concatenate: Join a sequence of arrays along an existing axis.
hsplit: Split array along second axis.
block: Assemble arrays from blocks.

Notes

The function is applied to both the _data and the _mask, if any.

Examples

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