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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 expKEGG(organism,names_KEGGids):
 """ Gets all KEGG pathways for an organism :param organism: an organism as listed in organismsKEGG() :param names_KEGGids: a Pandas dataframe with the columns 'gene_name': and 'KEGGid' as reported from idsKEGG(organism) (or a subset of it). :returns df: a Pandas dataframe with 'KEGGid','pathID(1):
pathNAME(1)', 'pathID(n):
pathNAME(n)' :returns paths: a list of retrieved KEGG pathways """

    #print "KEGG API: http://rest.kegg.jp/list/pathway/"+organism
    #sys.stdout.flush()
    kegg_paths=urlopen("http://rest.kegg.jp/list/pathway/"+organism).read()
    kegg_paths=kegg_paths.split("\n")
    final=[]
    for k in kegg_paths:
        final.append(k.split("\t"))
    df=pd.DataFrame(final[0:len(final)-1])[[0,1]]

    df.columns=['pathID','pathName']
    print("Collecting genes for pathways")
    sys.stdout.flush()
    df_pg=pd.DataFrame()
    for i in df['pathID'].tolist():
        print(i)
        sys.stdout.flush()
        path_genes=urlopen("http://rest.kegg.jp/link/genes/"+i).read()
        path_genes=path_genes.split("\n")
        final=[]
        for k in path_genes:
            final.append(k.split("\t"))
        if len(final[0]) > 1:
            df_tmp=pd.DataFrame(final[0:len(final)-1])[[0,1]]
            df_tmp.columns=['pathID','KEGGid']
            df_pg=pd.concat([df_pg,df_tmp])
    df=pd.merge(df,df_pg,on=["pathID"], how="outer")
    df=df[df['KEGGid'].isin(names_KEGGids['KEGGid'].tolist())]
    df=pd.merge(df,names_KEGGids,how='left',on=['KEGGid'])
    df_fA=pd.DataFrame(columns=['KEGGid'])
    paths=[]
    for k in df[['pathID']].drop_duplicates()['pathID'].tolist():
        df_tmp=df[df['pathID']==k]
        pathName=df_tmp['pathName'].tolist()[0]
        pathName=" : ".join([k,pathName])
        keggIDs_in_path=df_tmp[['KEGGid']].drop_duplicates()['KEGGid'].tolist()
        a={pathName:keggIDs_in_path}
        a=pd.DataFrame(a,index=range(len(keggIDs_in_path)))
        a['KEGGid']=a[pathName].copy()
        df_fA=pd.merge(df_fA,a,how='outer',on=['KEGGid'])
        paths.append(pathName)

    return df_fA, paths