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?






intersection series elementwise-operations







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?






    intersection series elementwise-operations







    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?






      intersection series elementwise-operations







      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




      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
          1






          active

          oldest

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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

            votes








            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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