Numpy.hstack — NumPy V1.23 Manual

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numpy.hstack# numpy.hstack(tup, *, dtype=None, casting='same_kind')[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: 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. In the case of a single array_like input, it will be treated as a sequence of arrays; i.e., each element along the zeroth axis is treated as a separate array.

dtypestr or dtype

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

New in version 1.24.

casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

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

New in version 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).

unstack

Split an array into a tuple of sub-arrays along an axis.

Examples

Try it in your browser! >>> importnumpyasnp >>> 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]]) Go BackOpen In Tab On this page

• hstack