How to get element wise intersection from two Series in python pandas












1














My question is for python pandas.
I have two Series and each Series has elements of string as follows:
To simplify, I've concatenated two Series in DataFrame.



import pandas as pd
import numpy as np
my_df = pd.DataFrame([['ab', 'bz', 'b'], ['cd', 'ct', 'c'], ['ef', 'ka', np.nan]], columns=['sr_1', 'sr_2', 'intersection'])


enter image description here



Any ideas for this?










share|improve this question



























    1














    My question is for python pandas.
    I have two Series and each Series has elements of string as follows:
    To simplify, I've concatenated two Series in DataFrame.



    import pandas as pd
    import numpy as np
    my_df = pd.DataFrame([['ab', 'bz', 'b'], ['cd', 'ct', 'c'], ['ef', 'ka', np.nan]], columns=['sr_1', 'sr_2', 'intersection'])


    enter image description here



    Any ideas for this?










    share|improve this question

























      1












      1








      1







      My question is for python pandas.
      I have two Series and each Series has elements of string as follows:
      To simplify, I've concatenated two Series in DataFrame.



      import pandas as pd
      import numpy as np
      my_df = pd.DataFrame([['ab', 'bz', 'b'], ['cd', 'ct', 'c'], ['ef', 'ka', np.nan]], columns=['sr_1', 'sr_2', 'intersection'])


      enter image description here



      Any ideas for this?










      share|improve this question













      My question is for python pandas.
      I have two Series and each Series has elements of string as follows:
      To simplify, I've concatenated two Series in DataFrame.



      import pandas as pd
      import numpy as np
      my_df = pd.DataFrame([['ab', 'bz', 'b'], ['cd', 'ct', 'c'], ['ef', 'ka', np.nan]], columns=['sr_1', 'sr_2', 'intersection'])


      enter image description here



      Any ideas for this?







      intersection series elementwise-operations






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 12 '18 at 4:49









      Sang-il Ahn

      344




      344
























          1 Answer
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          2














          This is what you can do:



          import pandas as pd
          import numpy as np

          df1 = pd.DataFrame({'sr1' : ['ab','cd','ef'] ,
          'sr2' : ['bz','ct','ka',]})

          df1['intersection'] = df1.apply(lambda x: set(x.sr1) & set(x.sr2), axis=1)

          df1['intersection'] = df1.intersection.apply(lambda x: list(x)[0] if len(x)>0 else np.nan)


          The output:



          enter image description here






          share|improve this answer





















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            1 Answer
            1






            active

            oldest

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            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            This is what you can do:



            import pandas as pd
            import numpy as np

            df1 = pd.DataFrame({'sr1' : ['ab','cd','ef'] ,
            'sr2' : ['bz','ct','ka',]})

            df1['intersection'] = df1.apply(lambda x: set(x.sr1) & set(x.sr2), axis=1)

            df1['intersection'] = df1.intersection.apply(lambda x: list(x)[0] if len(x)>0 else np.nan)


            The output:



            enter image description here






            share|improve this answer


























              2














              This is what you can do:



              import pandas as pd
              import numpy as np

              df1 = pd.DataFrame({'sr1' : ['ab','cd','ef'] ,
              'sr2' : ['bz','ct','ka',]})

              df1['intersection'] = df1.apply(lambda x: set(x.sr1) & set(x.sr2), axis=1)

              df1['intersection'] = df1.intersection.apply(lambda x: list(x)[0] if len(x)>0 else np.nan)


              The output:



              enter image description here






              share|improve this answer
























                2












                2








                2






                This is what you can do:



                import pandas as pd
                import numpy as np

                df1 = pd.DataFrame({'sr1' : ['ab','cd','ef'] ,
                'sr2' : ['bz','ct','ka',]})

                df1['intersection'] = df1.apply(lambda x: set(x.sr1) & set(x.sr2), axis=1)

                df1['intersection'] = df1.intersection.apply(lambda x: list(x)[0] if len(x)>0 else np.nan)


                The output:



                enter image description here






                share|improve this answer












                This is what you can do:



                import pandas as pd
                import numpy as np

                df1 = pd.DataFrame({'sr1' : ['ab','cd','ef'] ,
                'sr2' : ['bz','ct','ka',]})

                df1['intersection'] = df1.apply(lambda x: set(x.sr1) & set(x.sr2), axis=1)

                df1['intersection'] = df1.intersection.apply(lambda x: list(x)[0] if len(x)>0 else np.nan)


                The output:



                enter image description here







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 12 '18 at 5:19









                Pankaj Joshi

                926410




                926410






























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