How to use the stumpy.core.transpose_dataframe function in stumpy

The multi-dimensional matrix profile index where each row of the array
        corresponds to each matrix profile index for a given dimension.

    Notes
    -----

    `DOI: 10.1109/ICDM.2017.66 \
    `__

    See mSTAMP Algorithm
    """

    hosts = list(dask_client.ncores().keys())
    nworkers = len(hosts)

    T = np.asarray(core.transpose_dataframe(T))

    if T.ndim <= 1:  # pragma: no cover
        err = f"T is {T.ndim}-dimensional and must be greater than 1-dimensional"
        raise ValueError(f"{err}")

    core.check_dtype(T)
    core.check_nan(T)
    core.check_window_size(m)

    d = T.shape[0]
    n = T.shape[1]
    k = n - m + 1
    excl_zone = int(np.ceil(m / 4))  # See Definition 3 and Figure 3

    M_T, Σ_T = _multi_compute_mean_std(T, m)
    μ_Q, σ_Q = _multi_compute_mean_std(T, m)

1-D matrix profile and the second row is the 2-D matrix profile).

    I : ndarray
        The multi-dimensional matrix profile index where each row of the array
        corresponds to each matrix profile index for a given dimension.

    Notes
    -----

    `DOI: 10.1109/ICDM.2017.66 \
    `__

    See mSTAMP Algorithm
    """

    T = np.asarray(core.transpose_dataframe(T))

    if T.ndim <= 1:  # pragma: no cover
        err = f"T is {T.ndim}-dimensional and must be greater than 1-dimensional"
        raise ValueError(f"{err}")

    core.check_dtype(T)
    core.check_nan(T)
    core.check_window_size(m)

    d = T.shape[0]
    n = T.shape[1]
    k = n - m + 1
    excl_zone = int(np.ceil(m / 4))  # See Definition 3 and Figure 3

    M_T, Σ_T = _multi_compute_mean_std(T, m)
    μ_Q, σ_Q = _multi_compute_mean_std(T, m)