text: import pandas as pd
  import os
  
  # get all the different gene names in our expression data
  names = set()
  dir = 'Differential Expression results'
  for file_name in os.listdir(dir):
      file_path = os.path.join(dir, file_name)
      df = pd.read_csv(file_path)
      names.update([ens_id for ens_id, gene_name in df['Unnamed: 0'].str.split('_')])
  
  # export the ENSG names to use with biomart
  f = open('genes.txt', 'w')
  f.writelines([",".join(sorted(list(names)))])
  f.close()
  
  # result of biomart:
  df = pd.read_csv('genes_transcript_type_ncbi_kegg.csv')
  df.columns = ['gene_id', 'type', 'ncbi', 'kegg']
  df.to_csv('genes_transcript_type_ncbi_kegg.csv', index=False)
  
  # get only unique row per Gene stable ID
  unique = df.sort_values('gene_id').groupby(['gene_id']).first().reset_index()
  unique.to_csv('genes_transcript_type_ncbi_kegg_unique.csv', index=False)
  
  