def METHOD_NAME(data, sampling_rate=1000, method="auto"):
    """**EDA Analysis**
    Perform EDA analysis on either epochs (event-related analysis) or on longer periods of data
    such as resting-state data.
    Parameters
    ----------
    data : Union[dict, pd.DataFrame]
        A dictionary of epochs, containing one DataFrame per epoch, usually obtained via
        `epochs_create`, or a DataFrame containing all epochs, usually obtained via
        `epochs_to_df`. Can also take a DataFrame of processed signals from a longer period of
        data, typically generated by `eda_process` or `bio_process`. Can also take a dict
        containing sets of separate periods of data.
    sampling_rate : int
        The sampling frequency of the signal (in Hz, i.e., samples/second).
        Defaults to 1000Hz.
    method : str
        Can be one of ``"event-related"`` for event-related analysis on epochs, or
        ``"interval-related"`` for analysis on longer periods of data. Defaults to ``"auto"`` where
        the right method will be chosen based on the mean duration of the data (``"event-related"``
        for duration under 10s).
    Returns
    -------
    DataFrame
        A dataframe containing the analyzed EDA features. If event-related analysis is conducted,
        each epoch is indicated by the `Label` column. See :func:`eda_eventrelated` and
        :func:`eda_intervalrelated` docstrings for details.
    See Also
    --------
    .bio_process, eda_process, .epochs_create, eda_eventrelated, eda_intervalrelated
    Examples
    ----------
    * **Example 1: Data for event-related analysis**
    .. ipython:: python
      import neurokit2 as nk
      # Download the data for event-related analysis
      data = nk.data("bio_eventrelated_100hz")
      # Process the data for event-related analysis
      df, info = nk.bio_process(eda=data["EDA"], sampling_rate=100)
      events = nk.events_find(data["Photosensor"], threshold_keep='below',
                              event_conditions=["Negative", "Neutral", "Neutral", "Negative"])
      epochs = nk.epochs_create(df, events, sampling_rate=100, epochs_start=-0.1, epochs_end=1.9)
      # Analyze
      analyze_epochs = nk.eda_analyze(epochs, sampling_rate=100)
      analyze_epochs
    * **Example 2: Resting-state data**
    .. ipython:: python
      import neurokit2 as nk
      # Download the resting-state data
      data = nk.data("bio_resting_8min_100hz")
      # Process the data
      df, info = nk.eda_process(data["EDA"], sampling_rate=100)
      # Analyze
      analyze_df = nk.eda_analyze(df, sampling_rate=100)
      analyze_df
    """
    method = method.lower()
    # Event-related analysis
    if method in ["event-related", "event", "epoch"]:
        # Sanity checks
        if isinstance(data, dict):
            for i in data:
                colnames = data[i].columns.values
        elif isinstance(data, pd.DataFrame):
            colnames = data.columns.values
        if len([i for i in colnames if "Label" in i]) == 0:
            raise ValueError(
                "NeuroKit error: eda_analyze(): Wrong input or method, we couldn't extract epochs features."
            )
        else:
            features = eda_eventrelated(data)
    # Interval-related analysis
    elif method in ["interval-related", "interval", "resting-state"]:
        features = eda_intervalrelated(data)
    # Auto
    elif method in ["auto"]:
        if isinstance(data, dict):
            for i in data:
                duration = len(data[i]) / sampling_rate
            if duration >= 10:
                features = eda_intervalrelated(data)
            else:
                features = eda_eventrelated(data)
        if isinstance(data, pd.DataFrame):
            if "Label" in data.columns:
                epoch_len = data["Label"].value_counts()[0]
                duration = epoch_len / sampling_rate
            else:
                duration = len(data) / sampling_rate
            if duration >= 10:
                features = eda_intervalrelated(data)
            else:
                features = eda_eventrelated(data)
    return features