text: import pandas as pd
  import csv
  
  
  
  # PROVIDE LEVEL OF NUTS (NUTS1, NUTS2 or NUTS3)
  nuts_lvl = 2
  
  
  
  # Loading all collected infos 
  df = pd.read_csv('github_accounts.csv', delimiter = ',')
  df = df.drop(labels = 'Unnamed: 0', axis = 1)
  
  
  
  if nuts_lvl == 3:
      df['current_nuts'] = df['github_nuts3']
  elif nuts_lvl == 2:
      df['current_nuts'] = df['github_nuts3'].str[:4]
  elif nuts_lvl == 1:
      df['current_nuts'] = df['github_nuts3'].str[:3]
  elif nuts_lvl == 0:
      df['current_nuts'] = df['github_nuts3'].str[:2]
  
  
  
  # Extracting each location source that uniquely explains location and grouping by
  github_loc = df[(df['github_state'].notnull()) & (df['current_nuts'].notnull())]
  github_loc = github_loc[['current_nuts']].groupby(['current_nuts']).agg({'current_nuts': ['count']}).reset_index()
  github_loc.columns = github_loc.columns.map('_'.join)
  github_loc.columns = ['nuts3','github_count']
  
  twitter_loc = df[(df['twitter_state'].notnull()) & (df['current_nuts'].notnull())]
  twitter_loc = twitter_loc[['current_nuts']].groupby(['current_nuts']).agg({'current_nuts': ['count']}).reset_index()
  twitter_loc.columns = twitter_loc.columns.map('_'.join)
  twitter_loc.columns = ['nuts3','twitter_count']
  
  
  
  # Merging the datasets
  export = github_loc.merge(twitter_loc, how = 'outer')
  
  
  
  # Some fine work on the df
  export = export.fillna(0)
  export[['github_count', 'twitter_count']] = export[['github_count', 'twitter_count']].astype(int)
  
  
  
  # Summing up locations of all 
  export['total'] = export['github_count'] + export['twitter_count']
  
  
  
  # Exporting stats
  export = export.sort_values(by=['total'], ascending=False)
  export.to_csv('nuts%s_stats.csv' % nuts_lvl, sep=',')
  