text: """
  Advocate Health Care (Illinois)
  
  Source URL: https://www.advocatehealth.com/about-us/financial-assistance-for-patients/hospital-pricing-information
  """
  
  import pandas as pd
  import xml.etree.ElementTree as ET
  
  def to_common_format_df():
  
      # read_xml() is new in pandas 1.3.0
      df = pd.read_xml('https://www.advocatehealth.com/assets/documents/hospital-pricing-information/362169147_advocate-christ-medical-center_standardcharges.xml')
      non_rate_cols = ['facility', 'type', 'chargecode_drg_cpt', 'description', 'rev', 'cpt']
      rate_cols = set(df.pipe(tweak_df).columns) - set(non_rate_cols)
  
      # re-shape data from wide (payer rates as columns) to tall
      df = (df.pipe(tweak_df)
              .melt(id_vars=['source', 'code_type', 'code', 'code_desc'], 
                    value_vars=rate_cols, 
                    var_name='rate_amt_type',
                    value_name='rate_amt')
           )    
      
      return df
  
  def tweak_df(df):
      """Edits to dataframe:
          * edit columns names to lower-case and fill spaces with underscores
          * coalesce 'rev', 'chargecode_drg_cpt', and 'cpt' columns into one 'code' column
          * rename and drop columns
      """
      df.columns = [col.lower().replace(' ', '_') for col in df.columns]
      df = (df.assign(code=df['rev'].combine_first(df['chargecode_drg_cpt']).combine_first(df['cpt']))
              .rename(columns={'description': 'code_desc', 'type': 'code_type', 'facility': 'source'})
              .drop(columns=['chargecode_drg_cpt', 'rev', 'cpt'])
           )
      return df
  
  if __name__ == "__main__":
      """
      Sample usage as submodule:
      $ python -im mrf_parser.sources.src_00003
      >>> df = to_common_format_df()
      """
      pass