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.










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.










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.










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






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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





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    1 Answer
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    active

    oldest

    votes








    1 Answer
    1






    active

    oldest

    votes









    active

    oldest

    votes






    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


























      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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