Use issubset to compare set values between two pandas dataframe columns











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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!










share|improve this question




























    up vote
    4
    down vote

    favorite












    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!










    share|improve this question


























      up vote
      4
      down vote

      favorite









      up vote
      4
      down vote

      favorite











      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!










      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






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      edited Nov 20 at 0:16









      jpp

      86.2k194898




      86.2k194898










      asked Nov 19 at 23:36









      S M

      212




      212
























          2 Answers
          2






          active

          oldest

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          up vote
          2
          down vote













          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 at 18:24




















          up vote
          2
          down vote













          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








            up vote
            2
            down vote













            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 at 18:24

















            up vote
            2
            down vote













            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 at 18:24















            up vote
            2
            down vote










            up vote
            2
            down vote









            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 at 0:28

























            answered Nov 20 at 0:15









            jpp

            86.2k194898




            86.2k194898












            • 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 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 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 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 at 18:24














            up vote
            2
            down vote













            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



























              up vote
              2
              down vote













              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

























                up vote
                2
                down vote










                up vote
                2
                down vote









                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 at 14:50

























                answered Nov 19 at 23:46









                sacul

                28.5k41639




                28.5k41639






























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