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Module « numpy.matlib »

Fonction stack - module numpy.matlib

Signature de la fonction stack

def stack(arrays, axis=0, out=None, *, dtype=None, casting='same_kind') 

Description

help(numpy.matlib.stack)

Join a sequence of arrays along a new axis.

The ``axis`` parameter specifies the index of the new axis in the
dimensions of the result. For example, if ``axis=0`` it will be the first
dimension and if ``axis=-1`` it will be the last dimension.

Parameters
----------
arrays : sequence of ndarrays
    Each array must have the same shape. In the case of a single ndarray
    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.

axis : int, optional
    The axis in the result array along which the input arrays are stacked.

out : ndarray, optional
    If provided, the destination to place the result. The shape must be
    correct, matching that of what stack would have returned if no
    out argument were specified.

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 stacked array has one more dimension than the input arrays.

See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
block : Assemble an nd-array from nested lists of blocks.
split : Split array into a list of multiple sub-arrays of equal size.
unstack : Split an array into a tuple of sub-arrays along an axis.

Examples
--------
>>> import numpy as np
>>> rng = np.random.default_rng()
>>> arrays = [rng.normal(size=(3,4)) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)

>>> np.stack(arrays, axis=1).shape
(3, 10, 4)

>>> np.stack(arrays, axis=2).shape
(3, 4, 10)

>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
       [4, 5, 6]])

>>> np.stack((a, b), axis=-1)
array([[1, 4],
       [2, 5],
       [3, 6]])

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