In Pandas, Using Isin To Match Dataframe To Other Dataframe

I have 2 dataframes: local_PC_user_filer_OpCode_sum: client_op clienthostid eventSum feeling usersidid 0 5030 1 1 Happy 5 1 5030

Solution 1:

You can use join:

cols = ['usersidid', 'clienthostid']
a = local_PC_user_filer_OpCode_sum.set_index(cols)
print (df_old_enough_users.join(a, on=cols, lsuffix='_x')[local_PC_user_filer_OpCode_sum.columns].reset_index(drop=True))

   client_op  clienthostid  eventSum  filerid feeling  usersidid
05030111   Happy          515030121     Mad          5

isin solution does not work, because columns and index matching is necessary too in both DataFrames.

Solution 2:

If you are interested in modifying @jezrael's answer, this might give you a cleaner answer.

df = pd.merge(local_PC_user_filer_OpCode_sum, 
              df_old_enough_users[['usersidid','clienthostid']], 
              on=['usersidid','clienthostid'], 
              how="right")["client_op", "clienthostid", "eventSum",  "filerid", "timestamp", "usersidid"]

df will have the exact columns from your original local_PC_user_filer_OpCode_sum dataframe, and the rows returned will only be on the right table that you used as the filter.