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.
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

>>> import numpy as np
>>> 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]])