text: Title: Cannot chose variables which become dummy variables when setting them in pandas
  Body: I have a dataset with a few categories that are coded as being the presence or absence of an intervention.  I am fitting a simple linear regression using OLS.  Multiple interventions may be present at the same time so I am adding an effect for each effect.  However the dummy variable is not being encoded the way I want it to be and it make the effects hard to interpret.  
  
  
  
          data = {
      "cat1": [0,0,0,1,1,1,1],
      "cat2": [0,0,0,0,0,1,1],
      "cat3": [1,1,0,0,1,1,1],
      "cat4": [0,1,0,1,1,1,1],
      }
  
      #load data into a DataFrame object:
      dftest = pd.DataFrame(data)
  
      # variable to store the label mapping into
      label_map = {}
      
      for fact in dftest.columns :
          dftest[fact], label_map[fact] = pd.factorize(dftest[fact])
          print(label_map[fact] )
  
  
  Produces the following dummy coding output...
  
      Int64Index([0, 1], dtype='int64')
      Int64Index([0, 1], dtype='int64')
      Int64Index([1, 0], dtype='int64')
      Int64Index([0, 1], dtype='int64')
  
  **Question 1)**
  How do I ensure that the 0 in the original mapping is always the dummy?  
  
  **Question 2)**
  If I code an interaction effect between  in statsmodels will that dummy carry over to the interaction?  I have another categorical with many levels column which in the model has an interaction with each of the other effects, I don't care what is the dummy in that category but I do care that it's interaction effects use the 0 from the other categories as their dummy i.e. their reference.
  
  **Question 3)** 
  If it's not possible to fix can I just reverse the effect sizes of the resulting model where appropriate?  So if 1 is the dummy for that effect then I just multiply the effect sizes by -1?
  