<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def combine_express_output(fnL, column='eff_counts', names=None, tg=None, define_sample_name=None, debug=False):
 """ Combine eXpress output files Parameters: fnL : list of strs of filenames List of paths to results.xprs files. column : string Column name of eXpress output to combine. names : list of strings Names to use for columns of output files. Overrides define_sample_name if provided. tg : string File with transcript-to-gene mapping. Transcripts should be in first column and genes in second column. define_sample_name : function that takes string as input Function mapping filename to sample name (or basename). For instance, you may have the basename in the path and use a regex to extract it. The basenames will be used as the column names. If this is not provided, the columns will be named as the input files. debug : boolean Passing True will trigger any debugging statements. """

    if names is not None:
        assert len(names) == len(fnL)
    if define_sample_name is None:
        define_sample_name = lambda x: x
    
    transcriptL = []
    for i,fn in enumerate(fnL):
        if names is not None:
            bn = names[i]
        else:
            bn = define_sample_name(fn)
        tDF = pd.read_table(fn, index_col=1, header=0)
        se = tDF[column]
        se.name = bn
        transcriptL.append(se)
    transcriptDF = pd.DataFrame(transcriptL).T
    transcriptDF.index.name = 'transcript'
    # There should not be any missing values.
    if transcriptDF.shape != transcriptDF.dropna().shape:
        sys.stderr.write('''Missing values in eXpress output. Check that the
                         same reference was used for all output files.\n''')
        sys.exit(1)

    if tg is not None:
        tgDF = pd.read_table(tg,
                             index_col=0,
                             header=None,
                             names=['gene_id'])
        import copy
        geneDF = copy.deepcopy(transcriptDF)
        geneDF['gene'] = tgDF.ix[geneDF.index]
        geneDF = geneDF.groupby('gene').sum()
        return transcriptDF, geneDF
    else:
        return transcriptDF, None