bundles / numpy 2.4.4 / numpy / ma / extras / hstack
function
numpy.ma.extras:hstack
source: /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 ndarraysThe 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 dtypeIf provided, the destination array will have this dtype. Cannot be provided together with
out.casting: {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optionalControls what kind of data casting may occur. Defaults to 'same_kind'.
Returns
stacked: ndarrayThe 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))✓
See also
- block
Assemble an nd-array from nested lists of blocks.
- column_stack
Stack 1-D arrays as columns into a 2-D array.
- concatenate
Join a sequence of arrays along an existing axis.
- dstack
Stack arrays in sequence depth wise (along third axis).
- hsplit
Split an array into multiple sub-arrays horizontally (column-wise).
- stack
Join a sequence of arrays along a new axis.
- unstack
Split an array into a tuple of sub-arrays along an axis.
- vstack
Stack arrays in sequence vertically (row wise).
Aliases
-
numpy.ma.hstack