Pandas dataframe drop columns with no header

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6
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What's the most pythonic place to drop the columns in a dataframe where the header row is NaN? Preferably inplace.



There may or may not be data in the column.



df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6], np.NaN: [7,np.NaN,9])
df.dropna(axis='columns', inplace=True)


Doesn't do it as it looks at the data in the column.



Wanted output 



df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6])


Thanks in advance for the replies.






python pandas dataframe indexing nan







share|improve this question



























    up vote
    6
    down vote

    favorite












    What's the most pythonic place to drop the columns in a dataframe where the header row is NaN? Preferably inplace.



    There may or may not be data in the column.



    df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6], np.NaN: [7,np.NaN,9])
    df.dropna(axis='columns', inplace=True)


    Doesn't do it as it looks at the data in the column.



    Wanted output 



    df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6])


    Thanks in advance for the replies.






    python pandas dataframe indexing nan







    share|improve this question

























      up vote
      6
      down vote

      favorite









      up vote
      6
      down vote

      favorite











      What's the most pythonic place to drop the columns in a dataframe where the header row is NaN? Preferably inplace.



      There may or may not be data in the column.



      df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6], np.NaN: [7,np.NaN,9])
      df.dropna(axis='columns', inplace=True)


      Doesn't do it as it looks at the data in the column.



      Wanted output 



      df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6])


      Thanks in advance for the replies.






      python pandas dataframe indexing nan







      share|improve this question















      What's the most pythonic place to drop the columns in a dataframe where the header row is NaN? Preferably inplace.



      There may or may not be data in the column.



      df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6], np.NaN: [7,np.NaN,9])
      df.dropna(axis='columns', inplace=True)


      Doesn't do it as it looks at the data in the column.



      Wanted output 



      df = pd.DataFrame('col1': [1,2,np.NaN], 'col2': [4,5,6])


      Thanks in advance for the replies.






      python pandas dataframe indexing nan




      python pandas dataframe indexing nan






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 1 hour ago









      jpp

      67.2k173984




      67.2k173984










      asked 1 hour ago









      Joylove

      13210




      13210






















          2 Answers
          2






          active

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          up vote
          4
          down vote













          Simply try this



          df.drop(np.nan, axis=1, inplace=True)





          share|improve this answer






















          • why passing as a list?
            – pyd
            1 hour ago






          • 1




            habit :D. doesn't have to. thanks for making perfect!
            – bakka
            42 mins ago

















          up vote
          1
          down vote













          You can use pd.Index.dropna:



          df = df[df.columns.dropna()]

          print(df)

           col1 col2
          0 1.0 4
          1 2.0 5
          2 NaN 6





          share|improve this answer




















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            2 Answers
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            active

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            2 Answers
            2






            active

            oldest

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            active

            oldest

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            active

            oldest

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            up vote
            4
            down vote













            Simply try this



            df.drop(np.nan, axis=1, inplace=True)





            share|improve this answer






















            • why passing as a list?
              – pyd
              1 hour ago






            • 1




              habit :D. doesn't have to. thanks for making perfect!
              – bakka
              42 mins ago














            up vote
            4
            down vote













            Simply try this



            df.drop(np.nan, axis=1, inplace=True)





            share|improve this answer






















            • why passing as a list?
              – pyd
              1 hour ago






            • 1




              habit :D. doesn't have to. thanks for making perfect!
              – bakka
              42 mins ago












            up vote
            4
            down vote










            up vote
            4
            down vote









            Simply try this



            df.drop(np.nan, axis=1, inplace=True)





            share|improve this answer














            Simply try this



            df.drop(np.nan, axis=1, inplace=True)






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited 23 mins ago

























            answered 1 hour ago









            bakka

            3688




            3688











            • why passing as a list?
              – pyd
              1 hour ago






            • 1




              habit :D. doesn't have to. thanks for making perfect!
              – bakka
              42 mins ago
















            • why passing as a list?
              – pyd
              1 hour ago






            • 1




              habit :D. doesn't have to. thanks for making perfect!
              – bakka
              42 mins ago















            why passing as a list?
            – pyd
            1 hour ago




            why passing as a list?
            – pyd
            1 hour ago




            1




            1




            habit :D. doesn't have to. thanks for making perfect!
            – bakka
            42 mins ago




            habit :D. doesn't have to. thanks for making perfect!
            – bakka
            42 mins ago












            up vote
            1
            down vote













            You can use pd.Index.dropna:



            df = df[df.columns.dropna()]

            print(df)

             col1 col2
            0 1.0 4
            1 2.0 5
            2 NaN 6





            share|improve this answer
























              up vote
              1
              down vote













              You can use pd.Index.dropna:



              df = df[df.columns.dropna()]

              print(df)

               col1 col2
              0 1.0 4
              1 2.0 5
              2 NaN 6





              share|improve this answer






















                up vote
                1
                down vote










                up vote
                1
                down vote









                You can use pd.Index.dropna:



                df = df[df.columns.dropna()]

                print(df)

                 col1 col2
                0 1.0 4
                1 2.0 5
                2 NaN 6





                share|improve this answer












                You can use pd.Index.dropna:



                df = df[df.columns.dropna()]

                print(df)

                 col1 col2
                0 1.0 4
                1 2.0 5
                2 NaN 6






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered 1 hour ago









                jpp

                67.2k173984




                67.2k173984



























                     

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