old_contents: #!/usr/bin/env python
  ##
  ##  Copyright 2015 SRI International
  ##  License: https://ncanda.sri.com/software-license.txt
  ##
  ##  $Revision: 2110 $
  ##  $LastChangedBy: nicholsn $
  ##  $LastChangedDate: 2015-08-07 09:10:29 -0700 (Fri, 07 Aug 2015) $
  ##
  """
  NP Release Script
  ======================
  Generates a CSV file for all subjects included in NP analysis
  """
  import os
  
  import pandas as pd
  
  directory = "/fs/ncanda-share/releases/NCANDA_DATA_00019/summaries"
  
  nps_file = ["ataxia.csv", "cddr.csv", "clinical.csv", "cnp.csv", "dd100.csv",
  		    "dd1000.csv", "grooved_pegboard.csv", "ishihara.csv",
  			"landoltc.csv", "rey-o.csv", "wais4.csv", "wrat4.csv"]
  
  final_df = pd.read_csv(os.path.join(directory, "demographics.csv"),
                         index_col=['subject','arm','visit'])
  
  race_map = dict(native_american_american_indian=1,
  				asian=2,
  				pacific_islander=3,
  				african_american_black=4,
  				caucasian_white=5);
  
  # Create a series for each race where there is a 1 or 0 indicating if the
  # participant belongs to the race or not and append to the final_df
  for k, v in race_map.iteritems():
  	race_filter = final_df.race == v
  	final_df[k] = race_filter.apply(lambda x: 1 if x == True else 0)
  
  for i in nps_file:
  	df = pd.read_csv(os.path.join(directory, i),
  	                 index_col=['subject','arm','visit'])
  	final_df = pd.concat([final_df, df], axis=1)
  
  final_df.to_csv('np_release.csv')
  
new_contents: #!/usr/bin/env python
  ##
  ##  Copyright 2015 SRI International
  ##  License: https://ncanda.sri.com/software-license.txt
  ##
  ##  $Revision: 2110 $
  ##  $LastChangedBy: nicholsn $
  ##  $LastChangedDate: 2015-08-07 09:10:29 -0700 (Fri, 07 Aug 2015) $
  ##
  """
  NP Release Script
  ======================
  Generates a CSV file for all subjects included in NP analysis
  """
  import os
  
  import pandas as pd
  
  directory = "/fs/ncanda-share/releases/NCANDA_DATA_00019/summaries"
  
  nps_file = ["ataxia.csv", "cddr.csv", "clinical.csv", "cnp.csv", "dd100.csv",
  		    "dd1000.csv", "grooved_pegboard.csv", "ishihara.csv",
  			"landoltc.csv", "rey-o.csv", "wais4.csv", "wrat4.csv"]
  
  final_df = pd.read_csv(os.path.join(directory, "demographics.csv"),
                         index_col=['subject','arm','visit'])
  
  race_map = dict(native_american_american_indian=1,
  				asian=2,
  				pacific_islander=3,
  				african_american_black=4,
  				caucasian_white=5);
  
  # Create a series for each race where there is a 1 or 0 indicating if the
  # participant belongs to the race or not and append to the final_df
  for k, v in race_map.iteritems():
  	race_filter = final_df.race == v
  	final_df[k] = race_filter.apply(lambda x: 1 if x == True else 0)
  
  for i in nps_file:
  	df = pd.read_csv(os.path.join(directory, i),
  	                 index_col=['subject','arm','visit'])
  	final_df = pd.concat([final_df, df], axis=1)
  
  final_df = final_df.rename(columns={'cddr31':'CDDR_PastMo_Binge',
  									'cddr30':'CDDR_PastYr_BingeCDR'});
  
  final_df.to_csv('np_release.csv')
  
subject: Update to np_release_script.py