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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def rankagg(df, method="stuart"):
 """Return aggregated ranks. Implementation is ported from the RobustRankAggreg R package References: Kolde et al., 2012, DOI: 10.1093/bioinformatics/btr709 Stuart et al., 2003, DOI: 10.1126/science.1087447 Parameters df : pandas.DataFrame DataFrame with values to be ranked and aggregated Returns ------- pandas.DataFrame with aggregated ranks """

    rmat = pd.DataFrame(index=df.iloc[:,0])

    step = 1 / rmat.shape[0]
    for col in df.columns:
        rmat[col] = pd.DataFrame({col:np.arange(step, 1 + step, step)}, index=df[col]).loc[rmat.index]
    rmat = rmat.apply(sorted, 1, result_type="expand")
    p = rmat.apply(qStuart, 1)
    df = pd.DataFrame(
        {"p.adjust":multipletests(p, method="h")[1]}, 
        index=rmat.index).sort_values('p.adjust')     
    
    return df["p.adjust"]