`numpy.ma.extras:hstack`

source: build-install/usr/lib/python3.14/site-packages/numpy/_core/shape_base.py :293

Signature

def hstack ( tup , * , dtype = None , casting = same_kind )

Summary

Stack arrays in sequence horizontally (column wise).

Extended Summary

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. 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.
dtype : str 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

stacked : ndarray: The array formed by stacking the given arrays.

Notes

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

Examples

import numpy as np
a = np.array((1,2,3))
b = np.array((4,5,6))
np.hstack((a,b))
a = np.array([[1],[2],[3]])
b = np.array([[4],[5],[6]])
np.hstack((a,b))

✓

Aliases

numpy.ma.hstack