hstack(tup)

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.

Returns

stacked : ndarray

The array formed by stacking the given arrays.

Stack arrays in sequence horizontally (column wise).

See Also

block

Assemble an nd-array from nested lists of blocks.

column_stack

Stack 1-D arrays as columns into a 2-D array.

concatenate

Join a sequence of arrays along an existing axis.

dstack

Stack arrays in sequence depth wise (along third axis).

hsplit

Split an array into multiple sub-arrays horizontally (column-wise).

stack

Join a sequence of arrays along a new axis.

vstack

Stack arrays in sequence vertically (row 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]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.extras.dstack numpy.column_stack numpy.dstack dask.array.routines.hstack scipy.spatial._qhull.HalfspaceIntersection numpy.block numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.vstack numpy.split numpy.ma.extras.vstack numpy.ma.extras.column_stack

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