WORK IN PROGRESS
    
    Concat, merge, join, drop keys in dictionary of pandas.DataFrames
    into one pandas.DataFrame (data) and a pandas.Series (labels).
    
    Parameters
    ----------
    data : dict of pandas.DataFrames
        Result of icy.read()
    cfg : dict or str, optional
        Dictionary of actions to perform on data
        or str with path to YAML, that will be parsed.
    
    Returns
    -------
    data : pandas.DataFrame
        The aggregated dataset
    labels : pandas.Series
        The target variable for analysis of the dataset,
        can have fewer samples than the aggregated dataset
    
    Notes
    -----

def merge(data, cfg=None):
    """ WORK IN PROGRESS
    
    Concat, merge, join, drop keys in dictionary of pandas.DataFrames
    into one pandas.DataFrame (data) and a pandas.Series (labels).
    
    Parameters
    ----------
    data : dict of pandas.DataFrames
        Result of icy.read()
    cfg : dict or str, optional
        Dictionary of actions to perform on data
        or str with path to YAML, that will be parsed.
    
    Returns
    -------
    data : pandas.DataFrame
        The aggregated dataset
    labels : pandas.Series
        The target variable for analysis of the dataset,
        can have fewer samples than the aggregated dataset
    
    Notes
    -----
    
    """
    
    # go from a dict of dataframes (data) to one dataframe (data) and one series (labels)
    # pd.concat([df1, df2], join, join_axes, ignore_index) and pd.merge(left, right, how, on, suffixes)
    # should be easy to iterate from normalized tables to a fully joined set of dataframes
    
    if type(cfg) == str:
        cfg = _read_yaml(cfg)
    if cfg == None:
        cfg = _read_yaml('local/merge.yml')
        if cfg == None:
            print('creating merge.yml config file draft ...')
            cfg = {}
            # find all tables with identical column names
            # if no common key-col
            # concat along rows, add col (src)
            # e.g. chimps
            
            # find all tables with same length
            # if no duplicate column names
            # concat along columns
            
            # find master table (by length?)
            # from smalles to biggest table
            # find possible key-cols by uniques == len
            # find bigger tables with common column names -> cands
            # check for highest overlap-ratio of uniques -> cand (prefer smaller table if equal ratio)
            # join table on best cand
            # if ratio below treshold put table on unidentified list

            for key in data:
                cfg[key] = list(data[key].columns)
            with open('local/merge.yml', 'xt') as f:
                yaml.dump(cfg, f)
            cfg = _read_yaml('local/merge.yml')
    
    # if cfg == None:
    #     if not os.path.exists(default_cfg):
    #         create default_cfg draft
    #     else:
    #         join on default_cfg
    #         report join_result
    # else:
    #     join on cfg
    #     report join_result
    
    labels = None
    return data, labels