Pandas calculation works for only first quarter of all rows





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I've got a dataset with about 4mil observations. I'm doing a few simple transformations. However, the first couple works fine for all 4m obs, but the last few only works for the first 1.2m rows and then return NaN for all remaining rows. I can't see anything different about the data in those rows that this would be the case. I'm wondering if it's a memory issue or something, based on how I've written my code.



Anyway here's a short snippet. The first transformation below works fine for all 4m rows, the second one only works up to 1.2m rows then throws NaN. Any ideas?



Thanks!



#CREATE VAR FOR NUMBER OF PPL WHO'VE CLIMBED EACH ROUTE (SENDERS)

senders = routes.groupby(['route_id'])['user_id'].transform('nunique')

routes['senders'] = senders

routes = routes.reset_index()

routes = routes.drop(['index'], axis=1)



#CREATE VAR FOR WEIGHTED AVG RATING

avg_rating = routes.groupby(['route_id'])['rating'].mean().astype('float64')

routes['avg_rating'] = avg_rating.astype('float64')

routes['war'] = routes.sends * routes.avg_rating

routes = routes.reset_index()

routes = routes.drop(['index'], axis=1)





pandas-groupby nan mean







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    0















    I've got a dataset with about 4mil observations. I'm doing a few simple transformations. However, the first couple works fine for all 4m obs, but the last few only works for the first 1.2m rows and then return NaN for all remaining rows. I can't see anything different about the data in those rows that this would be the case. I'm wondering if it's a memory issue or something, based on how I've written my code.



    Anyway here's a short snippet. The first transformation below works fine for all 4m rows, the second one only works up to 1.2m rows then throws NaN. Any ideas?



    Thanks!



    #CREATE VAR FOR NUMBER OF PPL WHO'VE CLIMBED EACH ROUTE (SENDERS)
    
senders = routes.groupby(['route_id'])['user_id'].transform('nunique')
    
routes['senders'] = senders
    
routes = routes.reset_index()
    
routes = routes.drop(['index'], axis=1)
    

    
#CREATE VAR FOR WEIGHTED AVG RATING
    
avg_rating = routes.groupby(['route_id'])['rating'].mean().astype('float64')
    
routes['avg_rating'] = avg_rating.astype('float64')
    
routes['war'] = routes.sends * routes.avg_rating
    
routes = routes.reset_index()
    
routes = routes.drop(['index'], axis=1)





    pandas-groupby nan mean







    share|improve this question



























      0












      0








      0








      I've got a dataset with about 4mil observations. I'm doing a few simple transformations. However, the first couple works fine for all 4m obs, but the last few only works for the first 1.2m rows and then return NaN for all remaining rows. I can't see anything different about the data in those rows that this would be the case. I'm wondering if it's a memory issue or something, based on how I've written my code.



      Anyway here's a short snippet. The first transformation below works fine for all 4m rows, the second one only works up to 1.2m rows then throws NaN. Any ideas?



      Thanks!



      #CREATE VAR FOR NUMBER OF PPL WHO'VE CLIMBED EACH ROUTE (SENDERS)
      
senders = routes.groupby(['route_id'])['user_id'].transform('nunique')
      
routes['senders'] = senders
      
routes = routes.reset_index()
      
routes = routes.drop(['index'], axis=1)
      

      
#CREATE VAR FOR WEIGHTED AVG RATING
      
avg_rating = routes.groupby(['route_id'])['rating'].mean().astype('float64')
      
routes['avg_rating'] = avg_rating.astype('float64')
      
routes['war'] = routes.sends * routes.avg_rating
      
routes = routes.reset_index()
      
routes = routes.drop(['index'], axis=1)





      pandas-groupby nan mean







      share|improve this question
















      I've got a dataset with about 4mil observations. I'm doing a few simple transformations. However, the first couple works fine for all 4m obs, but the last few only works for the first 1.2m rows and then return NaN for all remaining rows. I can't see anything different about the data in those rows that this would be the case. I'm wondering if it's a memory issue or something, based on how I've written my code.



      Anyway here's a short snippet. The first transformation below works fine for all 4m rows, the second one only works up to 1.2m rows then throws NaN. Any ideas?



      Thanks!



      #CREATE VAR FOR NUMBER OF PPL WHO'VE CLIMBED EACH ROUTE (SENDERS)
      
senders = routes.groupby(['route_id'])['user_id'].transform('nunique')
      
routes['senders'] = senders
      
routes = routes.reset_index()
      
routes = routes.drop(['index'], axis=1)
      

      
#CREATE VAR FOR WEIGHTED AVG RATING
      
avg_rating = routes.groupby(['route_id'])['rating'].mean().astype('float64')
      
routes['avg_rating'] = avg_rating.astype('float64')
      
routes['war'] = routes.sends * routes.avg_rating
      
routes = routes.reset_index()
      
routes = routes.drop(['index'], axis=1)





      pandas-groupby nan mean




      pandas-groupby nan mean






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Jan 3 at 6:26









      Zain Farooq

      2,06121030




      2,06121030










      asked Jan 3 at 4:16









      KeesKees

      5718




      5718
























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