how to fetch the latest row while joining two datasets and the latest row should be less than the date from the dataset 'A' [duplicate]

Consider I have a dataset which has a date column that is generated everyday as given below.

DF_A            ID  name    qty   date1   abc     20   17/01/20221   abc     10   18/01/20222   def     10   24/01/20222   def     40   25/01/20222   def     67   26/01/2022DF_BID  name    price_dt    price1   abc   18/01/2022    23.561   abc   17/01/2022    10.561   abc   16/01/2022    44.331   abc   15/01/2022    56.112   def   25/01/2022    2.982   def   26/01/2022    4.922   def   27/01/2022    4.882   def   24/01/2022    3.332   def   23/01/2022    8.472   def   22/01/2022    3.89

I'm joining the DF_A with DF_B and I only need the recent price_dt record that is less than the date column. This can be done my joining the 2 DF's and dropping the duplicates by sorting the price_dt DESC but the challenge is the DF size is so huge and joining is not feasible. so im looking to reduce the rows in DF_B before joining.

Code that I tried

DF_C =  pd.merge(DF_A,DF_B,on='ID',how='left')# (This actually give 40 rows which is not optimum way of doing for larger dataset)Expected_DF = DF_C.sort_values(by=['price_dt'], ascending=False)Expected_DF = Expected_DF.drop_duplicates(subset=['ID','name','date'],keep='first')

Expected_DF:

ID  name    qty    date     price_dt    price1   abc    20   17/01/2022  16/01/2022  44.331   abc    10   18/01/2022  17/01/2022  10.562   def    10   24/01/2022  23/01/2022  8.472   def    40   25/01/2022  24/01/2022  3.332   def    67   26/01/2022  25/01/2022  2.98

Im looking for feasible method when I can reduce the memory usage instead of fetching all the matching records from DF_B