code: def verified_approved(pronac, dt):
      
      items_df = data.approved_verified_items
      items_df = items_df.loc[items_df['PRONAC'] == pronac]
      items_df[[APPROVED_COLUMN, VERIFIED_COLUMN]] = items_df[
          [APPROVED_COLUMN, VERIFIED_COLUMN]
      ].astype(float)
      items_df["Item"] = items_df["Item"].str.replace("\r", "")
      items_df["Item"] = items_df["Item"].str.replace("\n", "")
      items_df["Item"] = items_df["Item"].str.replace('"', "")
      items_df["Item"] = items_df["Item"].str.replace("'", "")
      items_df["Item"] = items_df["Item"].str.replace("\\", "")
  
      THRESHOLD = 1.5
      bigger_than_approved = items_df[VERIFIED_COLUMN] > (
          items_df[APPROVED_COLUMN] * THRESHOLD
      )
  
      features = items_df[bigger_than_approved]
      outlier_items = outlier_items_(features)
      features_size = features.shape[0]
      is_outlier = features_size > 0
      return {
          "is_outlier": is_outlier,
          "valor": features_size,
          "maximo_esperado": MIN_EXPECTED_ITEMS,
          "minimo_esperado": MAX_EXPECTED_ITEMS,
          "lista_de_comprovantes": outlier_items,
          "link_da_planilha": "http://salic.cultura.gov.br/projeto/#/{0}/relacao-de-pagamento".format(pronac)
      }
docs: This metric compare budgetary items of SALIC projects in terms of
      verified versus approved value
      Items that have vlComprovacao > vlAprovacao * 1.5 are considered outliers
      output:
              is_outlier: True if any item is outlier
              valor: Absolute number of items that are outliers
              outlier_items: Outlier items detail