Function transpose

numpy/_core/fromnumeric.py:605–677

Returns an array with axes transposed. For a 1-D array, this returns an unchanged view of the original array, as a transposed vector is simply the same vector. To convert a 1-D array into a 2-D column vector, an additional dimension must be added, e.g., ``np.atleast_2d(a).T`` a

(a, axes=None)

@array_function_dispatch(_transpose_dispatcher)
def transpose(a, axes=None):
    """
    Returns an array with axes transposed.

    For a 1-D array, this returns an unchanged view of the original array, as a
    transposed vector is simply the same vector.
    To convert a 1-D array into a 2-D column vector, an additional dimension
    must be added, e.g., ``np.atleast_2d(a).T`` achieves this, as does
    ``a[:, np.newaxis]``.
    For a 2-D array, this is the 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, then
    ``transpose(a).shape == a.shape[::-1]``.

    Parameters
    ----------
    a : array_like
        Input array.
    axes : tuple or list of ints, optional
        If specified, it must be a tuple or list which contains a permutation
        of [0, 1, ..., N-1] where N is the number of axes of `a`. Negative
        indices can also be used to specify axes. The i-th axis of the returned
        array will correspond to the axis numbered ``axes[i]`` of the input.
        If not specified, defaults to ``range(a.ndim)[::-1]``, which reverses
        the order of the axes.

    Returns
    -------
    p : ndarray
        `a` with its axes permuted. A view is returned whenever possible.

    See Also
    --------
    ndarray.transpose : Equivalent method.
    moveaxis : Move axes of an array to new positions.
    argsort : Return the indices that would sort an array.

    Notes
    -----
    Use ``transpose(a, argsort(axes))`` to invert the transposition of tensors
    when using the `axes` keyword argument.

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

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