text: import pandas as pd
  
  
  train_stances = pd.read_csv('fnc-1/train_stances.csv')
  train_bodies = pd.read_csv('fnc-1/train_bodies.csv')
  
  
  
  LABELS = {'unrelated': 0, 'discuss': 1, 'disagree': 2, 'agree': 3}
  
  def get_val(row): 
      #bid = row['Body ID']
      val = train_stances['Stance'][train_stances['Body ID'] == row]
      print('heey')
      print(val)
      return val
  
  # train_stances['Stance'] = train_stances['Stance'].apply(lambda x: LABELS[x])
  # # train_bodies['Stance'] = train_bodies.apply(get_val)
  # train_bodies['Stance'] = train_bodies['Body ID'].apply(lambda x: get_val(x))
  # train_stances.to_csv('fnc-1/changed/ntrain_headings.csv')
  # train_bodies.to_csv('fnc-1/changed/ntrain_bodies.csv')
  
  def get_body(bid) :
      body  = train_bodies['articleBody'][train_bodies['Body ID'] == bid].item()
      return body
  
  train_stances['Stance'] = train_stances['Stance'].apply(lambda x: LABELS[x])
  print(train_bodies.columns)
  train_stances['Body'] = train_stances['Body ID'].apply(lambda x: get_body(x))
  print(train_stances.head())
  # train_stances.to_csv('fnc-1/train_data.csv')
  data = pd.DataFrame({'Body ID': train_stances['Body ID'], 'Body': train_stances['Body'], 'Stance': train_stances['Stance']})
  train_stances = train_stances.drop(['Body'], axis = 1)
  
  train_stances['Headline'] = train_stances['Headline'].astype(str)
  train_stances['Body ID'] = pd.to_numeric(train_stances['Body ID'], downcast='integer')
  train_stances['Stance'] = pd.to_numeric(train_stances['Stance'], downcast='integer')
  # train_stances.to_csv('fnc-1/changed/ntrain_headings.csv')
  
  data['Body'] = data['Body'].astype(str)
  data['Body ID'] = pd.to_numeric(data['Body ID'], downcast='integer')
  data['Stance'] = pd.to_numeric(data['Stance'], downcast='integer')
  data.to_csv('fnc-1/changed/ntrain_bodies.csv.gz', compression = 'gzip')