text: # import pandas, and load the nls weeks worked and college data
  import pandas as pd
  pd.set_option('display.width', 80)
  pd.set_option('display.max_columns', 7)
  pd.set_option('display.max_rows', 100)
  pd.options.display.float_format = '{:,.0f}'.format
  nls97weeksworked = pd.read_csv("data/nls97weeksworked.csv")
  nls97colenr = pd.read_csv("data/nls97colenr.csv")
  
  # look at some of the nls data
  nls97weeksworked.sample(10, random_state=1)
  nls97weeksworked.shape
  
  nls97weeksworked.originalid.nunique()
  nls97colenr.sample(10, random_state=1)
  nls97colenr.shape
  nls97colenr.originalid.nunique()
  
  # check for unique ids
  nls97weeksworked.groupby(['originalid','year'])\
    ['originalid'].count().shape
  nls97colenr.groupby(['originalid','year'])\
    ['originalid'].count().shape
  
  # create a function to check id mismatches
  def checkmerge(dfleft, dfright, idvar):
    dfleft['inleft'] = "Y"
    dfright['inright'] = "Y"
    dfboth = pd.merge(dfleft[idvar + ['inleft']],\
      dfright[idvar + ['inright']], on=idvar, how="outer")
    dfboth.fillna('N', inplace=True)
    print(pd.crosstab(dfboth.inleft, dfboth.inright))
  
  checkmerge(nls97weeksworked.copy(),nls97colenr.copy(), ['originalid','year'])
  
  # use multiple merge-by columns
  nlsworkschool = pd.merge(nls97weeksworked, nls97colenr, on=['originalid','year'], how="inner")
  nlsworkschool.shape
  nlsworkschool.sample(10, random_state=1)