Pivot unique values from Column B to a new column after grouping Column A












1














I have the following dataframe: 



COL_A   COL_B

1   a

1   b

2   a

2   b

3   a

3   b

4   c

4   d

5   c

5   d


and I'm attempting to group the values in COL_A and pivot the unique values in COL_B to a new column 



COL_A   COL_B   COL_C

1   a   b

2   a   b

3   a   b

4   c   d

5   c   d


I've tried a bunch of different techniques with df.groupby and df.pivot but I can't seem to get my desired results.






python pandas







share|improve this question
























  • Does each unique COL_A value always occur exactly twice?
    – jpp
    Nov 11 at 2:29










  • In this case yes
    – jbrd435
    Nov 11 at 2:32
















1














I have the following dataframe: 



COL_A   COL_B

1   a

1   b

2   a

2   b

3   a

3   b

4   c

4   d

5   c

5   d


and I'm attempting to group the values in COL_A and pivot the unique values in COL_B to a new column 



COL_A   COL_B   COL_C

1   a   b

2   a   b

3   a   b

4   c   d

5   c   d


I've tried a bunch of different techniques with df.groupby and df.pivot but I can't seem to get my desired results.






python pandas







share|improve this question
























  • Does each unique COL_A value always occur exactly twice?
    – jpp
    Nov 11 at 2:29










  • In this case yes
    – jbrd435
    Nov 11 at 2:32














1












1








1







I have the following dataframe: 



COL_A   COL_B

1   a

1   b

2   a

2   b

3   a

3   b

4   c

4   d

5   c

5   d


and I'm attempting to group the values in COL_A and pivot the unique values in COL_B to a new column 



COL_A   COL_B   COL_C

1   a   b

2   a   b

3   a   b

4   c   d

5   c   d


I've tried a bunch of different techniques with df.groupby and df.pivot but I can't seem to get my desired results.






python pandas







share|improve this question















I have the following dataframe: 



COL_A   COL_B

1   a

1   b

2   a

2   b

3   a

3   b

4   c

4   d

5   c

5   d


and I'm attempting to group the values in COL_A and pivot the unique values in COL_B to a new column 



COL_A   COL_B   COL_C

1   a   b

2   a   b

3   a   b

4   c   d

5   c   d


I've tried a bunch of different techniques with df.groupby and df.pivot but I can't seem to get my desired results.






python pandas




python pandas






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 11 at 2:36









eyllanesc

72.8k93055




72.8k93055










asked Nov 11 at 2:27









jbrd435

175




175












  • Does each unique COL_A value always occur exactly twice?
    – jpp
    Nov 11 at 2:29










  • In this case yes
    – jbrd435
    Nov 11 at 2:32


















  • Does each unique COL_A value always occur exactly twice?
    – jpp
    Nov 11 at 2:29










  • In this case yes
    – jbrd435
    Nov 11 at 2:32
















Does each unique COL_A value always occur exactly twice?
– jpp
Nov 11 at 2:29




Does each unique COL_A value always occur exactly twice?
– jpp
Nov 11 at 2:29












In this case yes
– jbrd435
Nov 11 at 2:32




In this case yes
– jbrd435
Nov 11 at 2:32












1 Answer
1






active

oldest

votes


















1














Since each unique COL_A value always occurs exactly twice, you can reshape your series and feed to the pd.DataFrame constructor:



df = df.sort_values('COL_A')



arr = df['COL_B'].values.reshape(-1, 2)



res = pd.DataFrame({'COL_A': df['COL_A'].unique(),

                    'COL_B': arr[:, 0],

                    'COL_C': arr[:, 1]})



print(res)



   COL_A COL_B COL_C

0      1     a     b

1      2     a     b

2      3     a     b

3      4     c     d

4      5     c     d





share|improve this answer





















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    1 Answer
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    1 Answer
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    active

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    oldest

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    active

    oldest

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    1














    Since each unique COL_A value always occurs exactly twice, you can reshape your series and feed to the pd.DataFrame constructor:



    df = df.sort_values('COL_A')
    

    
arr = df['COL_B'].values.reshape(-1, 2)
    

    
res = pd.DataFrame({'COL_A': df['COL_A'].unique(),
    
                    'COL_B': arr[:, 0],
    
                    'COL_C': arr[:, 1]})
    

    
print(res)
    

    
   COL_A COL_B COL_C
    
0      1     a     b
    
1      2     a     b
    
2      3     a     b
    
3      4     c     d
    
4      5     c     d





    share|improve this answer


























      1














      Since each unique COL_A value always occurs exactly twice, you can reshape your series and feed to the pd.DataFrame constructor:



      df = df.sort_values('COL_A')
      

      
arr = df['COL_B'].values.reshape(-1, 2)
      

      
res = pd.DataFrame({'COL_A': df['COL_A'].unique(),
      
                    'COL_B': arr[:, 0],
      
                    'COL_C': arr[:, 1]})
      

      
print(res)
      

      
   COL_A COL_B COL_C
      
0      1     a     b
      
1      2     a     b
      
2      3     a     b
      
3      4     c     d
      
4      5     c     d





      share|improve this answer
























        1












        1








        1






        Since each unique COL_A value always occurs exactly twice, you can reshape your series and feed to the pd.DataFrame constructor:



        df = df.sort_values('COL_A')
        

        
arr = df['COL_B'].values.reshape(-1, 2)
        

        
res = pd.DataFrame({'COL_A': df['COL_A'].unique(),
        
                    'COL_B': arr[:, 0],
        
                    'COL_C': arr[:, 1]})
        

        
print(res)
        

        
   COL_A COL_B COL_C
        
0      1     a     b
        
1      2     a     b
        
2      3     a     b
        
3      4     c     d
        
4      5     c     d





        share|improve this answer












        Since each unique COL_A value always occurs exactly twice, you can reshape your series and feed to the pd.DataFrame constructor:



        df = df.sort_values('COL_A')
        

        
arr = df['COL_B'].values.reshape(-1, 2)
        

        
res = pd.DataFrame({'COL_A': df['COL_A'].unique(),
        
                    'COL_B': arr[:, 0],
        
                    'COL_C': arr[:, 1]})
        

        
print(res)
        

        
   COL_A COL_B COL_C
        
0      1     a     b
        
1      2     a     b
        
2      3     a     b
        
3      4     c     d
        
4      5     c     d






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 11 at 2:33









        jpp

        90.1k2052101




        90.1k2052101






























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