old_contents: # Data modelling challenge.
  
  __author__ = 'Remus Knowles <remknowles@gmail.com>'
  
  import pandas as pd
  
  F_DATA = r'data challenge test.csv'
  F_CORRELATIONS = r'correlations.csv'
  
  def main():
  	df = pd.read_csv(F_DATA)
  	df.drop('id', axis=1, inplace=True)
  
  	# Split off the data we want to put in to our model.
  	df = df[:60]
  	df_to_predict = df[60:]
  
  	# Check for any obvious correlation in explanatory variables.
  	print df.corr().to_csv(F_CORRELATIONS)
  
  if __name__ == '__main__':
  	main()
new_contents: # Data modelling challenge.
  
  __author__ = 'Remus Knowles <remknowles@gmail.com>'
  
  import pandas as pd
  
  F_DATA = r'data challenge test.csv'
  F_CORRELATIONS = r'correlations.csv'
  
  def main():
  	df = pd.read_csv(F_DATA)
  	df.drop('id', axis=1, inplace=True)
  
  	# Split off the data we want to put in to our model.
  	df = df[:60]
  	df_to_predict = df[60:]
  
  	x = df['groups']
  	y = df.drop('groups', axis=1)
  
  	x_train = x[:50]
  	x_test = x[50:]
  	y_train = y[:50]
  	y_test = y[50:]
  
  	print len(x_train), len(x_test)
  	print x_train.head()
  	print y_train.head()
  
  if __name__ == '__main__':
  	main()
subject: split data in to training and test sets