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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 biomaRtTOkegg(df):
 """ Transforms a pandas dataframe with the columns 'ensembl_gene_id','kegg_enzyme' :param df: a pandas dataframe with the following columns: 'ensembl_gene_id','kegg_enzyme' :returns: a pandas dataframe with the following columns: 'ensembl_gene_id','kegg_enzyme' """

    df=df.dropna()
    ECcols=df.columns.tolist()
    df.reset_index(inplace=True,drop=True)
    # field = ECsb[['kegg_enzyme']]
    field = pd.DataFrame(df['kegg_enzyme'].str.split('+',1).tolist())[1]
    field = pd.DataFrame(field)
    df=pd.concat([df[['ensembl_gene_id']],field],axis=1)
    df.columns=ECcols
    df.drop_duplicates(inplace=True)
    df.reset_index(inplace=True,drop=True)
    plus=df['kegg_enzyme'].tolist()
    plus=[ s for s in plus if "+" in s ]
    noPlus=df[~df['kegg_enzyme'].isin(plus)]
    plus=df[df['kegg_enzyme'].isin(plus)]
    noPlus.reset_index(inplace=True, drop=True)
    plus.reset_index(inplace=True, drop=True)
    for p in range(0,len(plus)):
        enz=plus.ix[p]['kegg_enzyme']
        enz=enz.split("+")
        enz=pd.DataFrame(enz)
        enz.colums=['kegg_enzyme']
        enz['ensembl_gene_id']=plus.ix[p]['kegg_enzyme']
        noPlus=pd.concat([noPlus,enz])
    noPlus=noPlus.drop_duplicates()
    noPlus=noPlus[['ensembl_gene_id','kegg_enzyme']]
    noPlus['fake']='ec:'
    noPlus['kegg_enzyme']=noPlus['fake']+noPlus['kegg_enzyme']
    noPlus=noPlus[['ensembl_gene_id','kegg_enzyme']]

    return noPlus