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-Darrays where it concatenates along the first axis. Rebuilds arrays dividedbyhsplit.

This function makes most sense for arrays with up to 3 dimensions. Forinstance, for pixel-data with a height (first axis), width (second axis),and r/g/b channels (third axis). The functionsconcatenate,stack andblock 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 singlearray_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 beprovided together without.

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

>>>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]])
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