numpy.ma.hstack#

ma.hstack(*args, **kwargs) = <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
tupsequence of ndarrays

The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.

dtypestr or dtype

If provided, the destination array will have this dtype. Cannot be provided together with out.

.. versionadded:: 1.24
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

Controls what kind of data casting may occur. Defaults to ‘same_kind’.

.. versionadded:: 1.24
Returns
stackedndarray

The array formed by stacking the given arrays.

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).

Notes

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

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]])