Pandas Dataframe: Finding entries that share values (e.g. all games that contain a player)











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I have a CSV file of game history for my badminton club. I would like to be able to find out information about games that contain a given player (e.g. who did "Bill" play most with?). Here's an example of what two rounds of three games might look like:



import pandas as pd

player_data = player_data = pd.DataFrame(data=[

('2018-06-12', 1, 1, 1, 'Adam'),

 ('2018-06-12', 1, 1, 2, 'Bill'),

 ('2018-06-12', 1, 1, 3, 'Cindy'),

 ('2018-06-12', 1, 1, 4, 'Derek'),

 ('2018-06-12', 1, 2, 1, 'Edward'),

 ('2018-06-12', 1, 2, 2, 'Fred'),

 ('2018-06-12', 1, 2, 3, 'George'),

 ('2018-06-12', 1, 2, 4, 'Harry'),

 ('2018-06-12', 1, 3, 1, 'Ian'),

 ('2018-06-12', 1, 3, 2, 'Jack'),

 ('2018-06-12', 1, 3, 3, 'Karl'),

 ('2018-06-12', 1, 3, 4, 'Laura'),

 ('2018-06-12', 2, 1, 1, 'Karl'),

 ('2018-06-12', 2, 1, 2, 'Cindy'),

 ('2018-06-12', 2, 1, 3, 'Bill'),

 ('2018-06-12', 2, 1, 4, 'Derek'),

 ('2018-06-12', 2, 2, 1, 'Max'),

 ('2018-06-12', 2, 2, 2, 'George'),

('2018-06-12', 2, 2, 3, 'Fred'),

 ('2018-06-12', 2, 2, 4, 'Ian'),

 ('2018-06-12', 2, 3, 1, 'Nigel'),

 ('2018-06-12', 3, 3, 2, 'Edward'),

 ('2018-06-12', 3, 3, 3, 'Harry'),

 ('2018-06-12', 3, 3, 4, 'Adam')],   

columns=['Date', 'Round #', 'Court #', 'Space', 'Name'])


However, as each row is an individual player's entry, simply locating by name, e.g.



player_data.loc[player_data['Name'] == 'Bill']


is just going to return only Bill's individual entries, like so:



    Date    Round # Court # Space   Name



    1 2018-06-12    1   1   2   Bill

    14 2018-06-12   2   1   3   Bill


... when what I want is a new dataframe that contains ALL entries of games that Bill has played in, such that in this case it would display as:



Date    Round # Court # Space   Name

0   2018-06-12  1   1   1   Adam

1   2018-06-12  1   1   2   Bill

2   2018-06-12  1   1   3   Cindy

3   2018-06-12  1   1   4   Derek

12  2018-06-12  2   1   1   Karl

13  2018-06-12  2   1   2   Cindy

14  2018-06-12  2   1   3   Bill

15  2018-06-12  2   1   4   Derek


I'm thinking it might be easier to convert the original dataframe to one where each entry is a separate game with all the player names for that game listed in a tuple, so then it'd be relatively simple to check "if name in Names"? e.g. 



Date    Round # Court # Names

    0   2018-06-12  1   1   (Adam, Bill, Cindy, Derek)


... but maybe that'd cause other problems.






python python-3.x pandas







share|improve this question


























    up vote
    4
    down vote

    favorite












    I have a CSV file of game history for my badminton club. I would like to be able to find out information about games that contain a given player (e.g. who did "Bill" play most with?). Here's an example of what two rounds of three games might look like:



    import pandas as pd
    
player_data = player_data = pd.DataFrame(data=[
    
('2018-06-12', 1, 1, 1, 'Adam'),
    
 ('2018-06-12', 1, 1, 2, 'Bill'),
    
 ('2018-06-12', 1, 1, 3, 'Cindy'),
    
 ('2018-06-12', 1, 1, 4, 'Derek'),
    
 ('2018-06-12', 1, 2, 1, 'Edward'),
    
 ('2018-06-12', 1, 2, 2, 'Fred'),
    
 ('2018-06-12', 1, 2, 3, 'George'),
    
 ('2018-06-12', 1, 2, 4, 'Harry'),
    
 ('2018-06-12', 1, 3, 1, 'Ian'),
    
 ('2018-06-12', 1, 3, 2, 'Jack'),
    
 ('2018-06-12', 1, 3, 3, 'Karl'),
    
 ('2018-06-12', 1, 3, 4, 'Laura'),
    
 ('2018-06-12', 2, 1, 1, 'Karl'),
    
 ('2018-06-12', 2, 1, 2, 'Cindy'),
    
 ('2018-06-12', 2, 1, 3, 'Bill'),
    
 ('2018-06-12', 2, 1, 4, 'Derek'),
    
 ('2018-06-12', 2, 2, 1, 'Max'),
    
 ('2018-06-12', 2, 2, 2, 'George'),
    
('2018-06-12', 2, 2, 3, 'Fred'),
    
 ('2018-06-12', 2, 2, 4, 'Ian'),
    
 ('2018-06-12', 2, 3, 1, 'Nigel'),
    
 ('2018-06-12', 3, 3, 2, 'Edward'),
    
 ('2018-06-12', 3, 3, 3, 'Harry'),
    
 ('2018-06-12', 3, 3, 4, 'Adam')],   
    
columns=['Date', 'Round #', 'Court #', 'Space', 'Name'])


    However, as each row is an individual player's entry, simply locating by name, e.g.



    player_data.loc[player_data['Name'] == 'Bill']


    is just going to return only Bill's individual entries, like so:



        Date    Round # Court # Space   Name
    

    
    1 2018-06-12    1   1   2   Bill
    
    14 2018-06-12   2   1   3   Bill


    ... when what I want is a new dataframe that contains ALL entries of games that Bill has played in, such that in this case it would display as:



    Date    Round # Court # Space   Name
    
0   2018-06-12  1   1   1   Adam
    
1   2018-06-12  1   1   2   Bill
    
2   2018-06-12  1   1   3   Cindy
    
3   2018-06-12  1   1   4   Derek
    
12  2018-06-12  2   1   1   Karl
    
13  2018-06-12  2   1   2   Cindy
    
14  2018-06-12  2   1   3   Bill
    
15  2018-06-12  2   1   4   Derek


    I'm thinking it might be easier to convert the original dataframe to one where each entry is a separate game with all the player names for that game listed in a tuple, so then it'd be relatively simple to check "if name in Names"? e.g. 



    Date    Round # Court # Names
    
    0   2018-06-12  1   1   (Adam, Bill, Cindy, Derek)


    ... but maybe that'd cause other problems.






    python python-3.x pandas







    share|improve this question
























      up vote
      4
      down vote

      favorite









      up vote
      4
      down vote

      favorite











      I have a CSV file of game history for my badminton club. I would like to be able to find out information about games that contain a given player (e.g. who did "Bill" play most with?). Here's an example of what two rounds of three games might look like:



      import pandas as pd
      
player_data = player_data = pd.DataFrame(data=[
      
('2018-06-12', 1, 1, 1, 'Adam'),
      
 ('2018-06-12', 1, 1, 2, 'Bill'),
      
 ('2018-06-12', 1, 1, 3, 'Cindy'),
      
 ('2018-06-12', 1, 1, 4, 'Derek'),
      
 ('2018-06-12', 1, 2, 1, 'Edward'),
      
 ('2018-06-12', 1, 2, 2, 'Fred'),
      
 ('2018-06-12', 1, 2, 3, 'George'),
      
 ('2018-06-12', 1, 2, 4, 'Harry'),
      
 ('2018-06-12', 1, 3, 1, 'Ian'),
      
 ('2018-06-12', 1, 3, 2, 'Jack'),
      
 ('2018-06-12', 1, 3, 3, 'Karl'),
      
 ('2018-06-12', 1, 3, 4, 'Laura'),
      
 ('2018-06-12', 2, 1, 1, 'Karl'),
      
 ('2018-06-12', 2, 1, 2, 'Cindy'),
      
 ('2018-06-12', 2, 1, 3, 'Bill'),
      
 ('2018-06-12', 2, 1, 4, 'Derek'),
      
 ('2018-06-12', 2, 2, 1, 'Max'),
      
 ('2018-06-12', 2, 2, 2, 'George'),
      
('2018-06-12', 2, 2, 3, 'Fred'),
      
 ('2018-06-12', 2, 2, 4, 'Ian'),
      
 ('2018-06-12', 2, 3, 1, 'Nigel'),
      
 ('2018-06-12', 3, 3, 2, 'Edward'),
      
 ('2018-06-12', 3, 3, 3, 'Harry'),
      
 ('2018-06-12', 3, 3, 4, 'Adam')],   
      
columns=['Date', 'Round #', 'Court #', 'Space', 'Name'])


      However, as each row is an individual player's entry, simply locating by name, e.g.



      player_data.loc[player_data['Name'] == 'Bill']


      is just going to return only Bill's individual entries, like so:



          Date    Round # Court # Space   Name
      

      
    1 2018-06-12    1   1   2   Bill
      
    14 2018-06-12   2   1   3   Bill


      ... when what I want is a new dataframe that contains ALL entries of games that Bill has played in, such that in this case it would display as:



      Date    Round # Court # Space   Name
      
0   2018-06-12  1   1   1   Adam
      
1   2018-06-12  1   1   2   Bill
      
2   2018-06-12  1   1   3   Cindy
      
3   2018-06-12  1   1   4   Derek
      
12  2018-06-12  2   1   1   Karl
      
13  2018-06-12  2   1   2   Cindy
      
14  2018-06-12  2   1   3   Bill
      
15  2018-06-12  2   1   4   Derek


      I'm thinking it might be easier to convert the original dataframe to one where each entry is a separate game with all the player names for that game listed in a tuple, so then it'd be relatively simple to check "if name in Names"? e.g. 



      Date    Round # Court # Names
      
    0   2018-06-12  1   1   (Adam, Bill, Cindy, Derek)


      ... but maybe that'd cause other problems.






      python python-3.x pandas







      share|improve this question













      I have a CSV file of game history for my badminton club. I would like to be able to find out information about games that contain a given player (e.g. who did "Bill" play most with?). Here's an example of what two rounds of three games might look like:



      import pandas as pd
      
player_data = player_data = pd.DataFrame(data=[
      
('2018-06-12', 1, 1, 1, 'Adam'),
      
 ('2018-06-12', 1, 1, 2, 'Bill'),
      
 ('2018-06-12', 1, 1, 3, 'Cindy'),
      
 ('2018-06-12', 1, 1, 4, 'Derek'),
      
 ('2018-06-12', 1, 2, 1, 'Edward'),
      
 ('2018-06-12', 1, 2, 2, 'Fred'),
      
 ('2018-06-12', 1, 2, 3, 'George'),
      
 ('2018-06-12', 1, 2, 4, 'Harry'),
      
 ('2018-06-12', 1, 3, 1, 'Ian'),
      
 ('2018-06-12', 1, 3, 2, 'Jack'),
      
 ('2018-06-12', 1, 3, 3, 'Karl'),
      
 ('2018-06-12', 1, 3, 4, 'Laura'),
      
 ('2018-06-12', 2, 1, 1, 'Karl'),
      
 ('2018-06-12', 2, 1, 2, 'Cindy'),
      
 ('2018-06-12', 2, 1, 3, 'Bill'),
      
 ('2018-06-12', 2, 1, 4, 'Derek'),
      
 ('2018-06-12', 2, 2, 1, 'Max'),
      
 ('2018-06-12', 2, 2, 2, 'George'),
      
('2018-06-12', 2, 2, 3, 'Fred'),
      
 ('2018-06-12', 2, 2, 4, 'Ian'),
      
 ('2018-06-12', 2, 3, 1, 'Nigel'),
      
 ('2018-06-12', 3, 3, 2, 'Edward'),
      
 ('2018-06-12', 3, 3, 3, 'Harry'),
      
 ('2018-06-12', 3, 3, 4, 'Adam')],   
      
columns=['Date', 'Round #', 'Court #', 'Space', 'Name'])


      However, as each row is an individual player's entry, simply locating by name, e.g.



      player_data.loc[player_data['Name'] == 'Bill']


      is just going to return only Bill's individual entries, like so:



          Date    Round # Court # Space   Name
      

      
    1 2018-06-12    1   1   2   Bill
      
    14 2018-06-12   2   1   3   Bill


      ... when what I want is a new dataframe that contains ALL entries of games that Bill has played in, such that in this case it would display as:



      Date    Round # Court # Space   Name
      
0   2018-06-12  1   1   1   Adam
      
1   2018-06-12  1   1   2   Bill
      
2   2018-06-12  1   1   3   Cindy
      
3   2018-06-12  1   1   4   Derek
      
12  2018-06-12  2   1   1   Karl
      
13  2018-06-12  2   1   2   Cindy
      
14  2018-06-12  2   1   3   Bill
      
15  2018-06-12  2   1   4   Derek


      I'm thinking it might be easier to convert the original dataframe to one where each entry is a separate game with all the player names for that game listed in a tuple, so then it'd be relatively simple to check "if name in Names"? e.g. 



      Date    Round # Court # Names
      
    0   2018-06-12  1   1   (Adam, Bill, Cindy, Derek)


      ... but maybe that'd cause other problems.






      python python-3.x pandas




      python python-3.x pandas






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 15 at 1:49









      Plato's Cave

      327




      327
























          2 Answers
          2






          active

          oldest

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          up vote
          3
          down vote



          accepted










          After the filter using merge 



          s1=player_data.loc[player_data['Name'] == 'Bill',['Date','Round #','Court #']]
          
s2=s1.merge(player_data,how='left')
          
s2
          
Out[12]: 
          
         Date  Round #  Court #  Space   Name
          
0  2018-06-12        1        1      1   Adam
          
1  2018-06-12        1        1      2   Bill
          
2  2018-06-12        1        1      3  Cindy
          
3  2018-06-12        1        1      4  Derek
          
4  2018-06-12        2        1      1   Karl
          
5  2018-06-12        2        1      2  Cindy
          
6  2018-06-12        2        1      3   Bill
          
7  2018-06-12        2        1      4  Derek





          share|improve this answer




























            up vote
            0
            down vote













            My method for this is :



            bill_player_data = player_data.loc[player_data['Name'] == 'Bill']
            
ro = bill_player_data['Round #']
            
co = bill_player_data['Court #']
            
bill = player_data.loc[player_data['Round #'].isin(ro)]
            
bill = bill.loc[bill['Court #'].isin(co)]
            
bill





            share|improve this answer























            • This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
              – Plato's Cave
              Nov 15 at 2:19










            • sorry, edited the code. I forgot to group the cour, cheers
              – Railey Shahril
              Nov 15 at 2:43











            Your Answer






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            2 Answers
            2






            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes








            up vote
            3
            down vote



            accepted










            After the filter using merge 



            s1=player_data.loc[player_data['Name'] == 'Bill',['Date','Round #','Court #']]
            
s2=s1.merge(player_data,how='left')
            
s2
            
Out[12]: 
            
         Date  Round #  Court #  Space   Name
            
0  2018-06-12        1        1      1   Adam
            
1  2018-06-12        1        1      2   Bill
            
2  2018-06-12        1        1      3  Cindy
            
3  2018-06-12        1        1      4  Derek
            
4  2018-06-12        2        1      1   Karl
            
5  2018-06-12        2        1      2  Cindy
            
6  2018-06-12        2        1      3   Bill
            
7  2018-06-12        2        1      4  Derek





            share|improve this answer

























              up vote
              3
              down vote



              accepted










              After the filter using merge 



              s1=player_data.loc[player_data['Name'] == 'Bill',['Date','Round #','Court #']]
              
s2=s1.merge(player_data,how='left')
              
s2
              
Out[12]: 
              
         Date  Round #  Court #  Space   Name
              
0  2018-06-12        1        1      1   Adam
              
1  2018-06-12        1        1      2   Bill
              
2  2018-06-12        1        1      3  Cindy
              
3  2018-06-12        1        1      4  Derek
              
4  2018-06-12        2        1      1   Karl
              
5  2018-06-12        2        1      2  Cindy
              
6  2018-06-12        2        1      3   Bill
              
7  2018-06-12        2        1      4  Derek





              share|improve this answer























                up vote
                3
                down vote



                accepted







                up vote
                3
                down vote



                accepted






                After the filter using merge 



                s1=player_data.loc[player_data['Name'] == 'Bill',['Date','Round #','Court #']]
                
s2=s1.merge(player_data,how='left')
                
s2
                
Out[12]: 
                
         Date  Round #  Court #  Space   Name
                
0  2018-06-12        1        1      1   Adam
                
1  2018-06-12        1        1      2   Bill
                
2  2018-06-12        1        1      3  Cindy
                
3  2018-06-12        1        1      4  Derek
                
4  2018-06-12        2        1      1   Karl
                
5  2018-06-12        2        1      2  Cindy
                
6  2018-06-12        2        1      3   Bill
                
7  2018-06-12        2        1      4  Derek





                share|improve this answer












                After the filter using merge 



                s1=player_data.loc[player_data['Name'] == 'Bill',['Date','Round #','Court #']]
                
s2=s1.merge(player_data,how='left')
                
s2
                
Out[12]: 
                
         Date  Round #  Court #  Space   Name
                
0  2018-06-12        1        1      1   Adam
                
1  2018-06-12        1        1      2   Bill
                
2  2018-06-12        1        1      3  Cindy
                
3  2018-06-12        1        1      4  Derek
                
4  2018-06-12        2        1      1   Karl
                
5  2018-06-12        2        1      2  Cindy
                
6  2018-06-12        2        1      3   Bill
                
7  2018-06-12        2        1      4  Derek






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 15 at 1:53









                W-B

                97.2k73162




                97.2k73162
























                    up vote
                    0
                    down vote













                    My method for this is :



                    bill_player_data = player_data.loc[player_data['Name'] == 'Bill']
                    
ro = bill_player_data['Round #']
                    
co = bill_player_data['Court #']
                    
bill = player_data.loc[player_data['Round #'].isin(ro)]
                    
bill = bill.loc[bill['Court #'].isin(co)]
                    
bill





                    share|improve this answer























                    • This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                      – Plato's Cave
                      Nov 15 at 2:19










                    • sorry, edited the code. I forgot to group the cour, cheers
                      – Railey Shahril
                      Nov 15 at 2:43















                    up vote
                    0
                    down vote













                    My method for this is :



                    bill_player_data = player_data.loc[player_data['Name'] == 'Bill']
                    
ro = bill_player_data['Round #']
                    
co = bill_player_data['Court #']
                    
bill = player_data.loc[player_data['Round #'].isin(ro)]
                    
bill = bill.loc[bill['Court #'].isin(co)]
                    
bill





                    share|improve this answer























                    • This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                      – Plato's Cave
                      Nov 15 at 2:19










                    • sorry, edited the code. I forgot to group the cour, cheers
                      – Railey Shahril
                      Nov 15 at 2:43













                    up vote
                    0
                    down vote










                    up vote
                    0
                    down vote









                    My method for this is :



                    bill_player_data = player_data.loc[player_data['Name'] == 'Bill']
                    
ro = bill_player_data['Round #']
                    
co = bill_player_data['Court #']
                    
bill = player_data.loc[player_data['Round #'].isin(ro)]
                    
bill = bill.loc[bill['Court #'].isin(co)]
                    
bill





                    share|improve this answer














                    My method for this is :



                    bill_player_data = player_data.loc[player_data['Name'] == 'Bill']
                    
ro = bill_player_data['Round #']
                    
co = bill_player_data['Court #']
                    
bill = player_data.loc[player_data['Round #'].isin(ro)]
                    
bill = bill.loc[bill['Court #'].isin(co)]
                    
bill






                    share|improve this answer














                    share|improve this answer



                    share|improve this answer








                    edited Nov 15 at 2:42

























                    answered Nov 15 at 2:10









                    Railey Shahril

                    184




                    184












                    • This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                      – Plato's Cave
                      Nov 15 at 2:19










                    • sorry, edited the code. I forgot to group the cour, cheers
                      – Railey Shahril
                      Nov 15 at 2:43


















                    • This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                      – Plato's Cave
                      Nov 15 at 2:19










                    • sorry, edited the code. I forgot to group the cour, cheers
                      – Railey Shahril
                      Nov 15 at 2:43
















                    This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                    – Plato's Cave
                    Nov 15 at 2:19




                    This didn't work: the 'bill' dataframe was the same as the original 'player_data' dataframe
                    – Plato's Cave
                    Nov 15 at 2:19












                    sorry, edited the code. I forgot to group the cour, cheers
                    – Railey Shahril
                    Nov 15 at 2:43




                    sorry, edited the code. I forgot to group the cour, cheers
                    – Railey Shahril
                    Nov 15 at 2:43


















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