code: def _replace_nans(self, data):
          # return data
          
          for c in data:
              dtype = data[c].dtype
              if dtype in (np.float32, np.float64):
                  if dtype == np.float32:
                      replacement = self.MISSING_VALUES['f']
                  else:
                      replacement = self.MISSING_VALUES['d']
                  data[c] = data[c].fillna(replacement)
  
          return data
docs: Checks floating point data columns for nans, and replaces these with
          the generic Stata for missing value (.)