Selecting rows that match a column in another dataframe-CodePudding

I have 2 dataframes df_Participants and df_Movements and I want to keep the rows in df_Movements only if a participant is on in df_Participants.

df_Participants:

         id                          
0    1053.0     
1    1052.0  
2    1049.0

df_Movements

     id           participant                    
0    3902         1053                   
1    3901         1053                                  
611  2763          979                   
612  2762          979

Expected results:

     id           participant                    
0    3902         1053                   
1    3901         1053

what I have tried so far:

remove_incomplete_submissions = True
if remove_incomplete_submissions:

    df_Movements = df_Movements.loc[df_Movements['participant'].isin(df_Participants['id'])]

When I check the number of unique participants, it does not match. I know this is simple, but I can't seem to notice the issue here.

CodePudding user response：

You can use merge:

new_df = df_Participants.merge(df_Movements, how='left', left_on='id', right_on='participant')

CodePudding user response：

Use isin to create a boolean mask and get rows that match the condition:

>>> df_Movements[df_Movements['participant'].isin(df_Participants['id'])]

     id  participant
0  3902         1053
1  3901         1053

Edit: as suggested too by @Ben.T in comments