Python Code Optimization (For-Loop & If-else) Advice for quicker computation time

Need to reduce the computation for the following python code which contains multiple if else statements. The code runs on a DataBricks so I'm open to Pyspark Solutions as well. Currently this code takes more than 1 hour to run. So any help would be appreciated.

unique_list_code: List of Unique code from concat_df['C_Code'] column used to filter rows of dataframe containing the code.
concat_df:Pandas DataFrame with 4 million records

unique_list_code = list(concat_df['C_Code'].unique())
MC_list =[]
SN_list =[]
AN_list = []
Nothing_list =[]
for i in range(0,len(unique_list_code)):
  
  print(unique_list_code[i])
  code_filtered_df = concat_df[concat_df['C_Code'] == unique_list_code[i]]
  
  #SN_Filter:
  SN_filter = code_filtered_df[(code_filtered_df['D_Type'] == 'SN') & (code_filtered_df['Comm_P'] == 'P-mail')]
  if len(SN_filter)>0:
    print("Found SN")
    SN_list.append(unique_list_code[i])
    clean_up(SN_filter)
  else:
    #AN_Filter
    AN_filter = code_filtered_df[(code_filtered_df['D_Type'] == 'AN') & (code_filtered_df['Comm_P'] == 'P-mail')]
    if len(AN_filter)>0:
      print("Found AN")
      AN_list.append(unique_list_code[i])
      clean_up(AN_filter)
    else:
      #MC_Check
      MF_filter = code_filtered_df[code_filtered_df['MC_Flag'] =='Y' ]
      MF_DNS_filter = MF_filter[~(((MF_filter['D_Type'] == 'AN')| (MF_filter['D_Type'] =='SN')) &  (MF_filter['Comm_P'] == 'DNS'))]
      
      if len(MF_DNS_filter)>0:
        print("Found MC")
        MC_list.append(unique_list_code[i])
        clean_up(MF_DNS_filter)
      else:
        print("Nothing Found")
        Nothing_list.append(unique_list_code[i])
        
        
    ```