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Module « numpy.matlib »
Signature de la fonction hstack
def hstack(tup, *, dtype=None, casting='same_kind')
Description
help(numpy.matlib.hstack)
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
----------
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.
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]])
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