old_contents: # -*- coding: utf-8 -*-
  '''
  MN: 11/04/16
  Simple OLS anaysis
  '''
  import pandas as pd
  import restate
  import statsmodels.api as sm
  import statsmodels.formula.api as smf
  
  # Load data
  print "Load data"
  df = pd.read_csv("data/items_otodom.pl_080416_10.csv", encoding = 'utf-8')
  print "Clean data"
  df = restate.clean_data(df)
  # leave only interesting observaions
  df=df[df.price >50]
  df=df[df.price<600]
  df=df[df['pow'] <250]
  df = df[df.rooms<=5]
      
  # Leave only non-missings
  df= df[ df.floor.notnull() & df.rooms.notnull()]
  # Fit regression model 
  result = smf.ols('price ~ pow + C(rooms) + C(floor) ', data=df).fit()
  
  # Inspect the results
  print result.summary()
  
new_contents: # -*- coding: utf-8 -*-
  '''
  MN: 11/04/16
  Simple OLS analysis
  '''
  import pandas as pd
  import restate
  import statsmodels.api as sm
  import statsmodels.formula.api as smf
  
  # Load data
  print "Load data"
  df = pd.read_csv("data/items_otodom.pl_080416_10.csv", encoding = 'utf-8')
  print "Clean data"
  df = restate.clean_data(df)
  # leave only interesting observaions
  df=df[df.price >50]
  df=df[df.price<600]
  df=df[df['pow'] <250]
  df = df[df.rooms<=5]
      
  # Leave only non-missings
  df= df[ df.floor.notnull() & df.rooms.notnull()]
  # Fit regression model 
  result = smf.ols('price ~ pow + C(rooms) + C(floor) ', data=df).fit()
  
  # Inspect the results
  print result.summary()
  
subject: Correct typos