old_contents: __author__ = 'moshebasanchig'
  
  from base_classes import Base
  import pandas as pd
  
  
  class Transformers(Base):
      def __init__(self):
          self._execution_plan = None
  
      def combine(self, stream1, stream2, left_on=None, right_on=None):
          """
          takes two input streams (pandas data frames) and joins them based on the keys dictionary
          """
          if self._execution_plan:
              self._execution_plan.append('combine', 'transform')
  
          if left_on and right_on:
              return pd.merge(stream1, stream2, left_on=left_on, right_on=right_on, how='inner')
          else:
              return None
  
      def aggregate(self, stream, group_by=None, operators=None):
          """
          takes an input stream (pandas data frame) and perform a group-by + aggregation.
          for instance: aggregate(data, group_by=['col1'], operators={'col2': 'sum'})
          """
          if self._execution_plan:
              self._execution_plan.append('aggregate', 'transform')
  
          if group_by and operators:
              return stream.groupby(group_by).agg(operators)
          else:
              return None
new_contents: __author__ = 'moshebasanchig'
  
  from base_classes import Base
  import pandas as pd
  
  
  class Transformers(Base):
      def __init__(self):
          self._execution_plan = None
  
      def combine(self, stream1, stream2, left_on=None, right_on=None, how='inner'):
          """
          takes two input streams (pandas data frames) and joins them based on the keys dictionary
          """
          if self._execution_plan:
              self._execution_plan.append('combine', 'transform')
  
          if left_on and right_on:
              return pd.merge(stream1, stream2, left_on=left_on, right_on=right_on, how=how)
          else:
              return None
  
      def aggregate(self, stream, group_by=None, operators=None):
          """
          takes an input stream (pandas data frame) and perform a group-by + aggregation.
          for instance: aggregate(data, group_by=['col1'], operators={'col2': 'sum'})
          """
          if self._execution_plan:
              self._execution_plan.append('aggregate', 'transform')
  
          if group_by and operators:
              return stream.groupby(group_by).agg(operators)
          else:
              return None
subject: add how to the transformers option