content: import luigi
  import pandas as pd
  
  from augment_data import AppendFeaturesAggregatedFromTeachersDatasetToSchool
  
  
  class SplitSchoolOutliersData(luigi.Task):
      input_task = AppendFeaturesAggregatedFromTeachersDatasetToSchool()
  
      def requires(self):
          return self.input_task
  
      def output(self):
          return {'average': luigi.LocalTarget('./dados/2013/TS_ESCOLA_average.csv'),
                  'outstanding': luigi.LocalTarget('./dados/2013/TS_ESCOLA_outstanding.csv')}
  
      def run(self):
          with self.input_task.output().open('r') as fp:
              escolas_pd = pd.read_csv(fp)
  
          escolas_statistics = escolas_pd['MEDIA_9EF_MT'].describe()
          math_avg, math_std = escolas_statistics.values[1], escolas_statistics.values[2]
  
          above_two_std_schools_indices = escolas_pd['MEDIA_9EF_MT'] > (math_avg + 2*math_std)
          below_two_std_schools_indices = escolas_pd['MEDIA_9EF_MT'] < (math_avg + 2*math_std)
  
          with self.output()['average'].open('w') as fp:
              escolas_pd[below_two_std_schools_indices].to_csv(fp)
          with self.output()['outstanding'].open('w') as fp:
              escolas_pd[above_two_std_schools_indices].to_csv(fp)