Classe « ndarray »

Méthode numpy.ctypeslib.ndarray.transpose

Signature de la méthode transpose

Description

transpose.__doc__

a.transpose(*axes)

    Returns a view of the array with axes transposed.

    For a 1-D array this has no effect, as a transposed vector is simply the
    same vector. To convert a 1-D array into a 2D column vector, an additional
    dimension must be added. `np.atleast2d(a).T` achieves this, as does
    `a[:, np.newaxis]`.
    For a 2-D array, this is a standard matrix transpose.
    For an n-D array, if axes are given, their order indicates how the
    axes are permuted (see Examples). If axes are not provided and
    ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
    ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.

    Parameters
    ----------
    axes : None, tuple of ints, or `n` ints

     * None or no argument: reverses the order of the axes.

     * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
       `i`-th axis becomes `a.transpose()`'s `j`-th axis.

     * `n` ints: same as an n-tuple of the same ints (this form is
       intended simply as a "convenience" alternative to the tuple form)

    Returns
    -------
    out : ndarray
        View of `a`, with axes suitably permuted.

    See Also
    --------
    ndarray.T : Array property returning the array transposed.
    ndarray.reshape : Give a new shape to an array without changing its data.

    Examples
    --------
    >>> a = np.array([[1, 2], [3, 4]])
    >>> a
    array([[1, 2],
           [3, 4]])
    >>> a.transpose()
    array([[1, 3],
           [2, 4]])
    >>> a.transpose((1, 0))
    array([[1, 3],
           [2, 4]])
    >>> a.transpose(1, 0)
    array([[1, 3],
           [2, 4]])