Use issubset to compare set values between two pandas dataframe columns












4















I have a pandas dataframe with two columns that are filled with pandas sets. I want to check that all values in one column are a subset of the other column. I thought the code below would work but it seems you cannot apply .issubset() to two series with sets.



Ex:



data = [[['one','orange','green'],['one','orange']],[['milk','honey'],['Clarke', 'honey']]]



df = pd.DataFrame(data, columns=['Column_1','Column_2'])



Are_all_column_2_values_valid = df.loc[:, 'Column_2'].apply(set).issubset(df.loc[:, 'Column_1'])



desired_output = pd.series([True,False])


All values in both sets will be strings.



Any help would greatly be appreciated!






python python-3.x pandas dataframe set







share|improve this question





























    4















    I have a pandas dataframe with two columns that are filled with pandas sets. I want to check that all values in one column are a subset of the other column. I thought the code below would work but it seems you cannot apply .issubset() to two series with sets.



    Ex:



    data = [[['one','orange','green'],['one','orange']],[['milk','honey'],['Clarke', 'honey']]]
    

    
df = pd.DataFrame(data, columns=['Column_1','Column_2'])
    

    
Are_all_column_2_values_valid = df.loc[:, 'Column_2'].apply(set).issubset(df.loc[:, 'Column_1'])
    

    
desired_output = pd.series([True,False])


    All values in both sets will be strings.



    Any help would greatly be appreciated!






    python python-3.x pandas dataframe set







    share|improve this question



























      4












      4








      4








      I have a pandas dataframe with two columns that are filled with pandas sets. I want to check that all values in one column are a subset of the other column. I thought the code below would work but it seems you cannot apply .issubset() to two series with sets.



      Ex:



      data = [[['one','orange','green'],['one','orange']],[['milk','honey'],['Clarke', 'honey']]]
      

      
df = pd.DataFrame(data, columns=['Column_1','Column_2'])
      

      
Are_all_column_2_values_valid = df.loc[:, 'Column_2'].apply(set).issubset(df.loc[:, 'Column_1'])
      

      
desired_output = pd.series([True,False])


      All values in both sets will be strings.



      Any help would greatly be appreciated!






      python python-3.x pandas dataframe set







      share|improve this question
















      I have a pandas dataframe with two columns that are filled with pandas sets. I want to check that all values in one column are a subset of the other column. I thought the code below would work but it seems you cannot apply .issubset() to two series with sets.



      Ex:



      data = [[['one','orange','green'],['one','orange']],[['milk','honey'],['Clarke', 'honey']]]
      

      
df = pd.DataFrame(data, columns=['Column_1','Column_2'])
      

      
Are_all_column_2_values_valid = df.loc[:, 'Column_2'].apply(set).issubset(df.loc[:, 'Column_1'])
      

      
desired_output = pd.series([True,False])


      All values in both sets will be strings.



      Any help would greatly be appreciated!






      python python-3.x pandas dataframe set




      python python-3.x pandas dataframe set






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 20 '18 at 0:16









      jpp

      101k2162111




      101k2162111










      asked Nov 19 '18 at 23:36









      S MS M

      212




      212
























          2 Answers
          2






          active

          oldest

          votes


















          2














          First ensure you actually have series of sets:



          df = df.apply(lambda x: x.apply(set))


          Then use the syntactic sugar <= for set.issubset:



          print(df['Column_2'] <= df['Column_1'])
          

          
0     True
          
1    False
          
dtype: bool





          share|improve this answer


























          • Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

            – S M
            Nov 20 '18 at 18:24





















          2














          You can use a list comprehension like this:



          >>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]
          
[True, False]


          Or as a Series:



          >>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))
          
0     True
          
1    False
          
dtype: bool





          share|improve this answer

























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            2 Answers
            2






            active

            oldest

            votes








            2 Answers
            2






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            2














            First ensure you actually have series of sets:



            df = df.apply(lambda x: x.apply(set))


            Then use the syntactic sugar <= for set.issubset:



            print(df['Column_2'] <= df['Column_1'])
            

            
0     True
            
1    False
            
dtype: bool





            share|improve this answer


























            • Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

              – S M
              Nov 20 '18 at 18:24


















            2














            First ensure you actually have series of sets:



            df = df.apply(lambda x: x.apply(set))


            Then use the syntactic sugar <= for set.issubset:



            print(df['Column_2'] <= df['Column_1'])
            

            
0     True
            
1    False
            
dtype: bool





            share|improve this answer


























            • Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

              – S M
              Nov 20 '18 at 18:24
















            2












            2








            2







            First ensure you actually have series of sets:



            df = df.apply(lambda x: x.apply(set))


            Then use the syntactic sugar <= for set.issubset:



            print(df['Column_2'] <= df['Column_1'])
            

            
0     True
            
1    False
            
dtype: bool





            share|improve this answer















            First ensure you actually have series of sets:



            df = df.apply(lambda x: x.apply(set))


            Then use the syntactic sugar <= for set.issubset:



            print(df['Column_2'] <= df['Column_1'])
            

            
0     True
            
1    False
            
dtype: bool






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Nov 20 '18 at 0:28

























            answered Nov 20 '18 at 0:15









            jppjpp

            101k2162111




            101k2162111













            • Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

              – S M
              Nov 20 '18 at 18:24





















            • Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

              – S M
              Nov 20 '18 at 18:24



















            Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

            – S M
            Nov 20 '18 at 18:24







            Interesting solution. I am trying to apply the set to two columns of a larger pandas dataframe. I tried: df['Column_1'] = df['Column_1'].apply(lambda x: x.apply(set)) but get an error 'AttributeError: 'list' object has no attribute 'apply'' Do you know how to fix this?

            – S M
            Nov 20 '18 at 18:24















            2














            You can use a list comprehension like this:



            >>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]
            
[True, False]


            Or as a Series:



            >>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))
            
0     True
            
1    False
            
dtype: bool





            share|improve this answer






























              2














              You can use a list comprehension like this:



              >>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]
              
[True, False]


              Or as a Series:



              >>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))
              
0     True
              
1    False
              
dtype: bool





              share|improve this answer




























                2












                2








                2







                You can use a list comprehension like this:



                >>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]
                
[True, False]


                Or as a Series:



                >>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))
                
0     True
                
1    False
                
dtype: bool





                share|improve this answer















                You can use a list comprehension like this:



                >>> [set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1)]
                
[True, False]


                Or as a Series:



                >>> pd.Series(set(v).issubset(i) for v, i in zip(df.Column_2, df.Column_1))
                
0     True
                
1    False
                
dtype: bool






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 20 '18 at 14:50

























                answered Nov 19 '18 at 23:46









                sacuLsacuL

                30.4k41941




                30.4k41941






























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