How to create a new column based on two other columns in Pandas?












0












$begingroup$


I am searching for a way to create a new column in my data. I have tried using iterows() but found it extremely time consuming in my dataset containing 40 lakh rows. So here is what I want.
Consider I have 2 columns:
Event ID, TeamID ,I want to find the no. of unique TeamID under each EventID as a new column. In other words, I want to find the number of teams participating in each event as a new column.






machine-learning python pandas dataframe







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  • $begingroup$
    Something like a groupby?
    $endgroup$
    – Matthieu Brucher
    Jan 3 at 16:59
















0












$begingroup$


I am searching for a way to create a new column in my data. I have tried using iterows() but found it extremely time consuming in my dataset containing 40 lakh rows. So here is what I want.
Consider I have 2 columns:
Event ID, TeamID ,I want to find the no. of unique TeamID under each EventID as a new column. In other words, I want to find the number of teams participating in each event as a new column.






machine-learning python pandas dataframe







share|improve this question









$endgroup$












  • $begingroup$
    Something like a groupby?
    $endgroup$
    – Matthieu Brucher
    Jan 3 at 16:59














0












0








0





$begingroup$


I am searching for a way to create a new column in my data. I have tried using iterows() but found it extremely time consuming in my dataset containing 40 lakh rows. So here is what I want.
Consider I have 2 columns:
Event ID, TeamID ,I want to find the no. of unique TeamID under each EventID as a new column. In other words, I want to find the number of teams participating in each event as a new column.






machine-learning python pandas dataframe







share|improve this question









$endgroup$




I am searching for a way to create a new column in my data. I have tried using iterows() but found it extremely time consuming in my dataset containing 40 lakh rows. So here is what I want.
Consider I have 2 columns:
Event ID, TeamID ,I want to find the no. of unique TeamID under each EventID as a new column. In other words, I want to find the number of teams participating in each event as a new column.






machine-learning python pandas dataframe




machine-learning python pandas dataframe






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Jan 3 at 16:56









Arjun ChandraArjun Chandra

31




31












  • $begingroup$
    Something like a groupby?
    $endgroup$
    – Matthieu Brucher
    Jan 3 at 16:59


















  • $begingroup$
    Something like a groupby?
    $endgroup$
    – Matthieu Brucher
    Jan 3 at 16:59
















$begingroup$
Something like a groupby?
$endgroup$
– Matthieu Brucher
Jan 3 at 16:59




$begingroup$
Something like a groupby?
$endgroup$
– Matthieu Brucher
Jan 3 at 16:59










2 Answers
2






active

oldest

votes


















1












$begingroup$

You can try something like this to get a new dataframe that has pairs of (EventID, TeamCount):



event_id_team_count = data.groupby('EventID').agg({'TeamID': lambda x: x.nunique()})

event_id_team_count.rename(columns={"TeamID": "TeamCount"}, inplace=True)


If you want to have this new column in the original dataframe, all you need to do is to join the original dataframe with the one you have just created:



data = data.join(other=event_id_team_count, on="EventID")





share|improve this answer









$endgroup$













  • $begingroup$
    Thanks a lot... It worked!!!
    $endgroup$
    – Arjun Chandra
    Jan 4 at 6:25



















0












$begingroup$


  1. Create a dictionary with the unique count of TeamID with respective to EventID


uCountDict = dict(data.groupby("EventID").TeamID.count())

uCountDict


Sample output
{'A': 4,
'C': 3,
'D': 2,
'F': 1
}




  1. Now create a new column with unique count with respective to TeamID using apply function


data["TeamCount"] = data.EventID.apply(lambda x : uCountDict[x])





share|improve this answer








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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1












    $begingroup$

    You can try something like this to get a new dataframe that has pairs of (EventID, TeamCount):



    event_id_team_count = data.groupby('EventID').agg({'TeamID': lambda x: x.nunique()})
    
event_id_team_count.rename(columns={"TeamID": "TeamCount"}, inplace=True)


    If you want to have this new column in the original dataframe, all you need to do is to join the original dataframe with the one you have just created:



    data = data.join(other=event_id_team_count, on="EventID")





    share|improve this answer









    $endgroup$













    • $begingroup$
      Thanks a lot... It worked!!!
      $endgroup$
      – Arjun Chandra
      Jan 4 at 6:25
















    1












    $begingroup$

    You can try something like this to get a new dataframe that has pairs of (EventID, TeamCount):



    event_id_team_count = data.groupby('EventID').agg({'TeamID': lambda x: x.nunique()})
    
event_id_team_count.rename(columns={"TeamID": "TeamCount"}, inplace=True)


    If you want to have this new column in the original dataframe, all you need to do is to join the original dataframe with the one you have just created:



    data = data.join(other=event_id_team_count, on="EventID")





    share|improve this answer









    $endgroup$













    • $begingroup$
      Thanks a lot... It worked!!!
      $endgroup$
      – Arjun Chandra
      Jan 4 at 6:25














    1












    1








    1





    $begingroup$

    You can try something like this to get a new dataframe that has pairs of (EventID, TeamCount):



    event_id_team_count = data.groupby('EventID').agg({'TeamID': lambda x: x.nunique()})
    
event_id_team_count.rename(columns={"TeamID": "TeamCount"}, inplace=True)


    If you want to have this new column in the original dataframe, all you need to do is to join the original dataframe with the one you have just created:



    data = data.join(other=event_id_team_count, on="EventID")





    share|improve this answer









    $endgroup$



    You can try something like this to get a new dataframe that has pairs of (EventID, TeamCount):



    event_id_team_count = data.groupby('EventID').agg({'TeamID': lambda x: x.nunique()})
    
event_id_team_count.rename(columns={"TeamID": "TeamCount"}, inplace=True)


    If you want to have this new column in the original dataframe, all you need to do is to join the original dataframe with the one you have just created:



    data = data.join(other=event_id_team_count, on="EventID")






    share|improve this answer












    share|improve this answer



    share|improve this answer










    answered Jan 3 at 17:32









    msklmskl

    712




    712












    • $begingroup$
      Thanks a lot... It worked!!!
      $endgroup$
      – Arjun Chandra
      Jan 4 at 6:25


















    • $begingroup$
      Thanks a lot... It worked!!!
      $endgroup$
      – Arjun Chandra
      Jan 4 at 6:25
















    $begingroup$
    Thanks a lot... It worked!!!
    $endgroup$
    – Arjun Chandra
    Jan 4 at 6:25




    $begingroup$
    Thanks a lot... It worked!!!
    $endgroup$
    – Arjun Chandra
    Jan 4 at 6:25











    0












    $begingroup$


    1. Create a dictionary with the unique count of TeamID with respective to EventID


    uCountDict = dict(data.groupby("EventID").TeamID.count())
    
uCountDict


    Sample output
    {'A': 4,
    'C': 3,
    'D': 2,
    'F': 1
    }




    1. Now create a new column with unique count with respective to TeamID using apply function


    data["TeamCount"] = data.EventID.apply(lambda x : uCountDict[x])





    share|improve this answer








    New contributor




    Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.






    $endgroup$


















      0












      $begingroup$


      1. Create a dictionary with the unique count of TeamID with respective to EventID


      uCountDict = dict(data.groupby("EventID").TeamID.count())
      
uCountDict


      Sample output
      {'A': 4,
      'C': 3,
      'D': 2,
      'F': 1
      }




      1. Now create a new column with unique count with respective to TeamID using apply function


      data["TeamCount"] = data.EventID.apply(lambda x : uCountDict[x])





      share|improve this answer








      New contributor




      Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






      $endgroup$
















        0












        0








        0





        $begingroup$


        1. Create a dictionary with the unique count of TeamID with respective to EventID


        uCountDict = dict(data.groupby("EventID").TeamID.count())
        
uCountDict


        Sample output
        {'A': 4,
        'C': 3,
        'D': 2,
        'F': 1
        }




        1. Now create a new column with unique count with respective to TeamID using apply function


        data["TeamCount"] = data.EventID.apply(lambda x : uCountDict[x])





        share|improve this answer








        New contributor




        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.






        $endgroup$




        1. Create a dictionary with the unique count of TeamID with respective to EventID


        uCountDict = dict(data.groupby("EventID").TeamID.count())
        
uCountDict


        Sample output
        {'A': 4,
        'C': 3,
        'D': 2,
        'F': 1
        }




        1. Now create a new column with unique count with respective to TeamID using apply function


        data["TeamCount"] = data.EventID.apply(lambda x : uCountDict[x])






        share|improve this answer








        New contributor




        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.









        share|improve this answer



        share|improve this answer






        New contributor




        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.









        answered 1 hour ago









        NoorNoor

        12




        12




        New contributor




        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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        New contributor





        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.






        Noor is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
        Check out our Code of Conduct.






























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