Create a new column based on the values of the column by ID












1















Please help me solve this problem. I searched online but couldn’t find the solution.



df

Id Fruit
1 Apple
1 Carrot
2 Apple
3 Carrot

list = [fruit, vegetable, both]


The question is, how to assign for each id, if id’s fruit column contains apple or carrot the list. If id contains apple then assign ID 1 fruit, if carrot only, then vegetable, if both then both.



I tried to use str.contains by row, but it doesn’t take ID column.



Result



Newdf

Id Fruit New_col
1 Apple Both
1 Carrot Both
2 Apple Fruit
3 Carrot Vegetable


Thank you a lot.










share|improve this question



























    1















    Please help me solve this problem. I searched online but couldn’t find the solution.



    df

    Id Fruit
    1 Apple
    1 Carrot
    2 Apple
    3 Carrot

    list = [fruit, vegetable, both]


    The question is, how to assign for each id, if id’s fruit column contains apple or carrot the list. If id contains apple then assign ID 1 fruit, if carrot only, then vegetable, if both then both.



    I tried to use str.contains by row, but it doesn’t take ID column.



    Result



    Newdf

    Id Fruit New_col
    1 Apple Both
    1 Carrot Both
    2 Apple Fruit
    3 Carrot Vegetable


    Thank you a lot.










    share|improve this question

























      1












      1








      1








      Please help me solve this problem. I searched online but couldn’t find the solution.



      df

      Id Fruit
      1 Apple
      1 Carrot
      2 Apple
      3 Carrot

      list = [fruit, vegetable, both]


      The question is, how to assign for each id, if id’s fruit column contains apple or carrot the list. If id contains apple then assign ID 1 fruit, if carrot only, then vegetable, if both then both.



      I tried to use str.contains by row, but it doesn’t take ID column.



      Result



      Newdf

      Id Fruit New_col
      1 Apple Both
      1 Carrot Both
      2 Apple Fruit
      3 Carrot Vegetable


      Thank you a lot.










      share|improve this question














      Please help me solve this problem. I searched online but couldn’t find the solution.



      df

      Id Fruit
      1 Apple
      1 Carrot
      2 Apple
      3 Carrot

      list = [fruit, vegetable, both]


      The question is, how to assign for each id, if id’s fruit column contains apple or carrot the list. If id contains apple then assign ID 1 fruit, if carrot only, then vegetable, if both then both.



      I tried to use str.contains by row, but it doesn’t take ID column.



      Result



      Newdf

      Id Fruit New_col
      1 Apple Both
      1 Carrot Both
      2 Apple Fruit
      3 Carrot Vegetable


      Thank you a lot.







      python pandas






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      share|improve this question










      asked Nov 21 '18 at 3:04









      PythonistaPythonista

      344




      344
























          1 Answer
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          Doing map first , then we just using transform nunqiue finding the the position should be 'both'



          s=df.Fruit.map({'Apple':'Fruit','Carrot':'Vegetable'})
          mask=s.groupby(df.Id).transform('nunique').eq(2)
          s[mask]='both'
          df['New']=s
          df
          Out[190]:
          Id Fruit New
          0 1 Apple both
          1 1 Carrot both
          2 2 Apple Fruit
          3 3 Carrot Vegetable





          share|improve this answer























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            1 Answer
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            1 Answer
            1






            active

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            active

            oldest

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            active

            oldest

            votes









            1














            Doing map first , then we just using transform nunqiue finding the the position should be 'both'



            s=df.Fruit.map({'Apple':'Fruit','Carrot':'Vegetable'})
            mask=s.groupby(df.Id).transform('nunique').eq(2)
            s[mask]='both'
            df['New']=s
            df
            Out[190]:
            Id Fruit New
            0 1 Apple both
            1 1 Carrot both
            2 2 Apple Fruit
            3 3 Carrot Vegetable





            share|improve this answer




























              1














              Doing map first , then we just using transform nunqiue finding the the position should be 'both'



              s=df.Fruit.map({'Apple':'Fruit','Carrot':'Vegetable'})
              mask=s.groupby(df.Id).transform('nunique').eq(2)
              s[mask]='both'
              df['New']=s
              df
              Out[190]:
              Id Fruit New
              0 1 Apple both
              1 1 Carrot both
              2 2 Apple Fruit
              3 3 Carrot Vegetable





              share|improve this answer


























                1












                1








                1







                Doing map first , then we just using transform nunqiue finding the the position should be 'both'



                s=df.Fruit.map({'Apple':'Fruit','Carrot':'Vegetable'})
                mask=s.groupby(df.Id).transform('nunique').eq(2)
                s[mask]='both'
                df['New']=s
                df
                Out[190]:
                Id Fruit New
                0 1 Apple both
                1 1 Carrot both
                2 2 Apple Fruit
                3 3 Carrot Vegetable





                share|improve this answer













                Doing map first , then we just using transform nunqiue finding the the position should be 'both'



                s=df.Fruit.map({'Apple':'Fruit','Carrot':'Vegetable'})
                mask=s.groupby(df.Id).transform('nunique').eq(2)
                s[mask]='both'
                df['New']=s
                df
                Out[190]:
                Id Fruit New
                0 1 Apple both
                1 1 Carrot both
                2 2 Apple Fruit
                3 3 Carrot Vegetable






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 21 '18 at 3:11









                Wen-BenWen-Ben

                114k83369




                114k83369
































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