Calculating NPS using Pandas











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1
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I am very, very new to Python and am playing around with how I would calculate an NPS score.



The calculation is:




(count of scores 9-10/total count of scores 0-10) - (count of scores
0-6/total count of scores 0-10) for each council.




Data Frame I am using:



enter image description here



The NPS would need to be calculated for each council separately.
This is my first post on here, hopefully it makes sense. If someone could point me in the right direction it would be much appreciated.



Cheers,
Ben.










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  • Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
    – Andreas
    Nov 22 at 7:57










  • @ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
    – pygo
    Nov 22 at 8:02












  • @pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
    – Ben Swann
    Nov 22 at 8:34










  • @BenSwann, yes indeed :-)
    – pygo
    Nov 22 at 9:25















up vote
1
down vote

favorite












I am very, very new to Python and am playing around with how I would calculate an NPS score.



The calculation is:




(count of scores 9-10/total count of scores 0-10) - (count of scores
0-6/total count of scores 0-10) for each council.




Data Frame I am using:



enter image description here



The NPS would need to be calculated for each council separately.
This is my first post on here, hopefully it makes sense. If someone could point me in the right direction it would be much appreciated.



Cheers,
Ben.










share|improve this question
























  • Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
    – Andreas
    Nov 22 at 7:57










  • @ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
    – pygo
    Nov 22 at 8:02












  • @pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
    – Ben Swann
    Nov 22 at 8:34










  • @BenSwann, yes indeed :-)
    – pygo
    Nov 22 at 9:25













up vote
1
down vote

favorite









up vote
1
down vote

favorite











I am very, very new to Python and am playing around with how I would calculate an NPS score.



The calculation is:




(count of scores 9-10/total count of scores 0-10) - (count of scores
0-6/total count of scores 0-10) for each council.




Data Frame I am using:



enter image description here



The NPS would need to be calculated for each council separately.
This is my first post on here, hopefully it makes sense. If someone could point me in the right direction it would be much appreciated.



Cheers,
Ben.










share|improve this question















I am very, very new to Python and am playing around with how I would calculate an NPS score.



The calculation is:




(count of scores 9-10/total count of scores 0-10) - (count of scores
0-6/total count of scores 0-10) for each council.




Data Frame I am using:



enter image description here



The NPS would need to be calculated for each council separately.
This is my first post on here, hopefully it makes sense. If someone could point me in the right direction it would be much appreciated.



Cheers,
Ben.







python pandas






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edited Nov 22 at 8:00









c-chavez

2,13321732




2,13321732










asked Nov 22 at 7:43









Ben Swann

223




223












  • Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
    – Andreas
    Nov 22 at 7:57










  • @ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
    – pygo
    Nov 22 at 8:02












  • @pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
    – Ben Swann
    Nov 22 at 8:34










  • @BenSwann, yes indeed :-)
    – pygo
    Nov 22 at 9:25


















  • Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
    – Andreas
    Nov 22 at 7:57










  • @ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
    – pygo
    Nov 22 at 8:02












  • @pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
    – Ben Swann
    Nov 22 at 8:34










  • @BenSwann, yes indeed :-)
    – pygo
    Nov 22 at 9:25
















Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
– Andreas
Nov 22 at 7:57




Welcome to Stack Overflow! Please take the tour, look around, and read through the Help Center, in particular How do I ask a good question? If you run into a specific problem, research it thoroughly, search thoroughly here, and if you're still stuck post your code and a description of the problem. Also, remember to include Minimum, Complete, Verifiable Example. People will be glad to help
– Andreas
Nov 22 at 7:57












@ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
– pygo
Nov 22 at 8:02






@ Ben, what is desired output, are you looking something where NPS is greater than equals to 9 ? However if you have tried something then put that as well as it will help what exactly you want to achieve.
– pygo
Nov 22 at 8:02














@pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
– Ben Swann
Nov 22 at 8:34




@pygo Do you mean what is the expected answer? Sorry, I am just getting to know the etiquette on Stack Overflow.
– Ben Swann
Nov 22 at 8:34












@BenSwann, yes indeed :-)
– pygo
Nov 22 at 9:25




@BenSwann, yes indeed :-)
– pygo
Nov 22 at 9:25












1 Answer
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1
down vote



accepted










Assuming data is in data.csv:



import pandas as pd
from collections import defaultdict

df = pd.read_csv('data.csv')

high_nps = defaultdict(lambda: 0)
low_nps = defaultdict(lambda: 0)

high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council]) for council in total_nps}

print(nps_score)


Prints:



{'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}





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    active

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    oldest

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    active

    oldest

    votes








    up vote
    1
    down vote



    accepted










    Assuming data is in data.csv:



    import pandas as pd
    from collections import defaultdict

    df = pd.read_csv('data.csv')

    high_nps = defaultdict(lambda: 0)
    low_nps = defaultdict(lambda: 0)

    high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
    low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
    total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

    nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council]) for council in total_nps}

    print(nps_score)


    Prints:



    {'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}





    share|improve this answer

























      up vote
      1
      down vote



      accepted










      Assuming data is in data.csv:



      import pandas as pd
      from collections import defaultdict

      df = pd.read_csv('data.csv')

      high_nps = defaultdict(lambda: 0)
      low_nps = defaultdict(lambda: 0)

      high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
      low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
      total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

      nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council]) for council in total_nps}

      print(nps_score)


      Prints:



      {'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}





      share|improve this answer























        up vote
        1
        down vote



        accepted







        up vote
        1
        down vote



        accepted






        Assuming data is in data.csv:



        import pandas as pd
        from collections import defaultdict

        df = pd.read_csv('data.csv')

        high_nps = defaultdict(lambda: 0)
        low_nps = defaultdict(lambda: 0)

        high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
        low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
        total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

        nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council]) for council in total_nps}

        print(nps_score)


        Prints:



        {'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}





        share|improve this answer












        Assuming data is in data.csv:



        import pandas as pd
        from collections import defaultdict

        df = pd.read_csv('data.csv')

        high_nps = defaultdict(lambda: 0)
        low_nps = defaultdict(lambda: 0)

        high_nps.update(dict(df[df['NPS'] >= 9].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
        low_nps.update(dict(df[df['NPS'] <= 6].groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values))
        total_nps = dict(df.groupby('CouncilName').count().reset_index()[['CouncilName', 'NPS']].values)

        nps_score = {council: (high_nps[council] - low_nps[council]) / float(total_nps[council]) for council in total_nps}

        print(nps_score)


        Prints:



        {'Council A': 0.0, 'Council B': -1.0, 'Council C': -1.0}






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 22 at 7:57









        andersource

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