Get the last rows for a unique value in a column from a dataframe - Pandas

Question

I have an excerpt of a dataframe df below:

    Player          Trial       Score                          
0   Josh              1         1     
1   Josh              2         3 
2   Josh              3         2 
3   Josh              4         2 
4   Josh              5         2 
5   Josh              6         5    
6   Josh              7         2 
0   Yuli              1         1     
1   Yuli              2         3 
2   Yuli              3         2 
3   Yuli              4         2 
4   Yuli              5         2 
0   Max               1         4     
1   Max               2         6   
2   Max               3         10   
3   Max               4         10  
4   Max               5         17  
5   Max               6         10  
6   Max               7         14  

I would like to select the last rows which has a unique value for the 'Player' column i.e.

   Player          Trial       Score    
0   Max               1         4     
1   Max               2         6   
2   Max               3         10   
3   Max               4         10  
4   Max               5         17  
5   Max               6         10  
6   Max               7         14  

I have tried df.groupby("Player").tail() but this only returns the last 5 rows for each player. I have also tried a rather lengthy method:

df.groupby('Trial').tail().loc[df['Player'] == df['Player'].unique()[-1]]

This gives me what I want but I am sure I am missing a cleaner method. Any help would be appreciated to achieve this. Thank you.

Answer 1

If i understand correctly you just want to get all the rows of the final player (i.e the name in the last row of your df in Player column.

last_player = df.Player.iloc[-1]
df.loc[df.Player==last_player]

which prints:

   Player  Trial  Score
7     Max      1      4
8     Max      2      6
9     Max      3     10
10    Max      4     10
11    Max      5     17
12    Max      6     10
13    Max      7     14

Be aware the sorting your df, in a different way, will highly likely alter the output of the above code.