old_contents: import pandas as pd
  import os
  from dataserv import RAW_DATA_DIR, LocalDB
  
  raw_data = os.listdir( RAW_DATA_FILE )
  currency_columns = LocalDB.currencycolumncheck
  date_columns = LocalDB.datecolumncheck
  conv_str2date = LocalDB.str2dateconv
  encodings = {
              'iso' : 'ISO-8859-1',
              'utf' : 'utf-8',
              'win' : 'cp1252',
              }
  currency = 'AUD'
  
  def dframe_dateconv(dframe, col):
      ''' Go through date columns and convert to date format.
      '''
      for row in range(0, len(dframe[col])):
          dframe[col][row] = conv_str2date(dframe[col][row])
  
  def dframe_currencystrip(dframe, col, currency=currency):
      '''Iterate through a pandas dataframe stripping off currency codes and
      recast to float type. Pass in col and code as strings.
      '''
  
      dframe.loc[:, col] = dframe[col].str.strip(code)
      dframe.loc[:, col] = dframe[col].str.strip('-' + code)
      dframe.loc[:, col] = dframe[col].str.replace(',', '')
      dframe.loc[:, col] = dframe[col].astype(float)
      return dframe
  
  def make_sheets():
      data_sheets = []
      try:
          data_sheets = [pd.read_csv(datafile) for datafile in raw_data]
      except UnicodeDecodeError:
          for encs in encodings:
              data_sheets = [pd.read_csv(datafile, encoding=enc) for datafile in raw_data]
      return data_sheets
  
  def clean_sheets(currency = currency):
      data_sheets = make_sheets()
      for dframe in data_sheets:
          for col in dframe.keys():
              if currency_columns(col) == True:
                  dframe_currencystrip(dframe, col, currency=currency)
              if date_columns(col) == True:
                  dframe_dateconv(dframe, col)
  
  
new_contents: import pandas as pd
  import os
  from dataserv import RAW_DATA_DIR, LocalDB
  
  raw_data = os.listdir( RAW_DATA_DIR )
  currency_columns = LocalDB.currencycolumncheck
  date_columns = LocalDB.datecolumncheck
  conv_str2date = LocalDB.str2dateconv
  encodings = {
              'iso' : 'ISO-8859-1',
              'utf' : 'utf-8',
              'win' : 'cp1252',
              }
  currency = 'AUD'
  
  def dframe_dateconv(dframe, col):
      ''' Go through date columns and convert to date format.
          dframe is a pandas dataframe object, col is target column of type string.
      '''
      for row in range(0, len(dframe[col])):
          dframe[col][row] = conv_str2date(dframe[col][row])
  
  def dframe_currencystrip(dframe, col, currency=currency):
      '''Iterate through a pandas dataframe stripping off currency codes and
      recast to float type. Pass in col and code as strings.
      '''
  
      dframe.loc[:, col] = dframe[col].str.strip(code)
      dframe.loc[:, col] = dframe[col].str.strip('-' + code)
      dframe.loc[:, col] = dframe[col].str.replace(',', '')
      dframe.loc[:, col] = dframe[col].astype(float)
      return dframe
  
  def make_sheets():
      ''' Goes through files in RAW_DATA_DIR and returns a dict of
          {filename: pandas.DataFrame(filename)}
      '''
      data_sheets = {}
      try:
          data_sheets = {datafile.split()[0]: pd.read_csv(datafile) for datafile in raw_data}
      except UnicodeDecodeError:
          for enc in encodings:
              data_sheets = {datafile.split()[0]: pd.read_csv(datafile, encoding=enc) for datafile in raw_data}
      return data_sheets
  
  def clean_sheets(currency = currency):
      ''' Uses make_sheets(), and then processes to convert money from string to float,
          and datetime strings into datetime objects.
          Returns a dict of {filename: pandas.DataFrame(filename)}
      '''
      data_sheets = make_sheets()
      for dframe in data_sheets:
          for col in dframe.keys():
              if currency_columns(col) == True:
                  dframe_currencystrip(dframe, col, currency=currency)
              if date_columns(col) == True:
                  dframe_dateconv(dframe, col)
      return data_sheets
  
subject: update docstrings for make_sheets() and clean_sheets()