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How to use math.log10 function on whole pandas dataframe



2019 Community Moderator ElectionHow to merge two dictionaries in a single expression?How do I check whether a file exists without exceptions?Using global variables in a functionHow to make a chain of function decorators?Renaming columns in pandasHow can I replace all the NaN values with Zero's in a column of a pandas dataframeDelete column from pandas DataFrame by column name“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandas










7















I want to take the logarithm of every value in a pandas dataframe. I have tried this but it does not work:



#Reading data from excel and rounding values on 2 decimal places
import math
import pandas as pd

data = pd.read_excel("DataSet.xls").round(2)
log_data= math.log10(data)


It gives me this error:




TypeError: must be real number, not DataFrame




Do you have any idea what to do?










share|improve this question




























    7















    I want to take the logarithm of every value in a pandas dataframe. I have tried this but it does not work:



    #Reading data from excel and rounding values on 2 decimal places
    import math
    import pandas as pd

    data = pd.read_excel("DataSet.xls").round(2)
    log_data= math.log10(data)


    It gives me this error:




    TypeError: must be real number, not DataFrame




    Do you have any idea what to do?










    share|improve this question


























      7












      7








      7








      I want to take the logarithm of every value in a pandas dataframe. I have tried this but it does not work:



      #Reading data from excel and rounding values on 2 decimal places
      import math
      import pandas as pd

      data = pd.read_excel("DataSet.xls").round(2)
      log_data= math.log10(data)


      It gives me this error:




      TypeError: must be real number, not DataFrame




      Do you have any idea what to do?










      share|improve this question
















      I want to take the logarithm of every value in a pandas dataframe. I have tried this but it does not work:



      #Reading data from excel and rounding values on 2 decimal places
      import math
      import pandas as pd

      data = pd.read_excel("DataSet.xls").round(2)
      log_data= math.log10(data)


      It gives me this error:




      TypeError: must be real number, not DataFrame




      Do you have any idea what to do?







      python pandas numpy






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited 2 days ago









      yatu

      12.7k31341




      12.7k31341










      asked 2 days ago









      AleksandarAleksandar

      988




      988






















          3 Answers
          3






          active

          oldest

          votes


















          18














          Use the numpy version, not math



          import numpy as np

          np.log10(df)





          share|improve this answer








          New contributor




          ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
          Check out our Code of Conduct.




















          • Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

            – Aleksandar
            2 days ago






          • 1





            (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

            – Christoph Burschka
            2 days ago











          • @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

            – Draconis
            2 days ago






          • 1





            @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

            – user2357112
            2 days ago


















          11














          From what it seems math.log10 cannot handle neither pandas dataframes nor ndarrays.



          So one option would be to go with numpy, which also includes a function to compute the base 10 logarithm, np.log10, and reconstruct the dataframe as pointed out in other solutions.



          Or if you want to go with math.log10, and the same would apply to other functions that cannot be directly vectorized, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Do note however that this solution will be slower than a vectorized approach using np.log10.




          Use case



          Here's an example of how this could be done using DataFrame.applymap:



          df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))

          print(df)
          a b c d e f
          0 3 4 1 1 2 1
          1 4 4 4 3 4 1
          2 4 3 3 1 4 1
          3 3 4 1 3 1 1
          4 1 2 3 4 2 1
          5 1 3 3 1 4 3

          df.applymap(math.log10)

          a b c d e f
          0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
          1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
          2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
          3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
          4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
          5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121



          For the numpy solution, you could take the np.log10 of the dataframe, and reconstruct it as:



          pd.DataFrame(np.log10(data), index=df.index, columns=df.columns)





          share|improve this answer
































            3














            You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.



            You can test it:



            df.applymap(math.log10)





            share|improve this answer
























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              3 Answers
              3






              active

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              3 Answers
              3






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              18














              Use the numpy version, not math



              import numpy as np

              np.log10(df)





              share|improve this answer








              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.




















              • Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

                – Aleksandar
                2 days ago






              • 1





                (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

                – Christoph Burschka
                2 days ago











              • @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

                – Draconis
                2 days ago






              • 1





                @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

                – user2357112
                2 days ago















              18














              Use the numpy version, not math



              import numpy as np

              np.log10(df)





              share|improve this answer








              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.




















              • Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

                – Aleksandar
                2 days ago






              • 1





                (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

                – Christoph Burschka
                2 days ago











              • @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

                – Draconis
                2 days ago






              • 1





                @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

                – user2357112
                2 days ago













              18












              18








              18







              Use the numpy version, not math



              import numpy as np

              np.log10(df)





              share|improve this answer








              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.










              Use the numpy version, not math



              import numpy as np

              np.log10(df)






              share|improve this answer








              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.









              share|improve this answer



              share|improve this answer






              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.









              answered 2 days ago









              ecortazarecortazar

              2715




              2715




              New contributor




              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.





              New contributor





              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.






              ecortazar is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
              Check out our Code of Conduct.












              • Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

                – Aleksandar
                2 days ago






              • 1





                (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

                – Christoph Burschka
                2 days ago











              • @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

                – Draconis
                2 days ago






              • 1





                @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

                – user2357112
                2 days ago

















              • Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

                – Aleksandar
                2 days ago






              • 1





                (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

                – Christoph Burschka
                2 days ago











              • @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

                – Draconis
                2 days ago






              • 1





                @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

                – user2357112
                2 days ago
















              Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

              – Aleksandar
              2 days ago





              Thanks my friend!!! Do you have any idea what to do if some values in pandas dataframe are 0? Then, I cant use log function

              – Aleksandar
              2 days ago




              1




              1





              (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

              – Christoph Burschka
              2 days ago





              (Note that if numpy has no equivalent of the function you want, you can also use np.vectorize(function) to turn any scalar function into a vector function.)

              – Christoph Burschka
              2 days ago













              @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

              – Draconis
              2 days ago





              @Aleksandar At that point, you'll have to decide what you want to happen to the zeros. Numpy can handle whatever you choose without difficulty.

              – Draconis
              2 days ago




              1




              1





              @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

              – user2357112
              2 days ago





              @ChristophBurschka: But if you do that, it's going to be way slower than a "natively" vectorized function, as well as producing silently wrong results if you aren't careful about having consistent output dtypes.

              – user2357112
              2 days ago













              11














              From what it seems math.log10 cannot handle neither pandas dataframes nor ndarrays.



              So one option would be to go with numpy, which also includes a function to compute the base 10 logarithm, np.log10, and reconstruct the dataframe as pointed out in other solutions.



              Or if you want to go with math.log10, and the same would apply to other functions that cannot be directly vectorized, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Do note however that this solution will be slower than a vectorized approach using np.log10.




              Use case



              Here's an example of how this could be done using DataFrame.applymap:



              df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))

              print(df)
              a b c d e f
              0 3 4 1 1 2 1
              1 4 4 4 3 4 1
              2 4 3 3 1 4 1
              3 3 4 1 3 1 1
              4 1 2 3 4 2 1
              5 1 3 3 1 4 3

              df.applymap(math.log10)

              a b c d e f
              0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
              1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
              2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
              3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
              4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
              5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121



              For the numpy solution, you could take the np.log10 of the dataframe, and reconstruct it as:



              pd.DataFrame(np.log10(data), index=df.index, columns=df.columns)





              share|improve this answer





























                11














                From what it seems math.log10 cannot handle neither pandas dataframes nor ndarrays.



                So one option would be to go with numpy, which also includes a function to compute the base 10 logarithm, np.log10, and reconstruct the dataframe as pointed out in other solutions.



                Or if you want to go with math.log10, and the same would apply to other functions that cannot be directly vectorized, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Do note however that this solution will be slower than a vectorized approach using np.log10.




                Use case



                Here's an example of how this could be done using DataFrame.applymap:



                df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))

                print(df)
                a b c d e f
                0 3 4 1 1 2 1
                1 4 4 4 3 4 1
                2 4 3 3 1 4 1
                3 3 4 1 3 1 1
                4 1 2 3 4 2 1
                5 1 3 3 1 4 3

                df.applymap(math.log10)

                a b c d e f
                0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
                1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
                2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
                3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
                4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
                5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121



                For the numpy solution, you could take the np.log10 of the dataframe, and reconstruct it as:



                pd.DataFrame(np.log10(data), index=df.index, columns=df.columns)





                share|improve this answer



























                  11












                  11








                  11







                  From what it seems math.log10 cannot handle neither pandas dataframes nor ndarrays.



                  So one option would be to go with numpy, which also includes a function to compute the base 10 logarithm, np.log10, and reconstruct the dataframe as pointed out in other solutions.



                  Or if you want to go with math.log10, and the same would apply to other functions that cannot be directly vectorized, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Do note however that this solution will be slower than a vectorized approach using np.log10.




                  Use case



                  Here's an example of how this could be done using DataFrame.applymap:



                  df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))

                  print(df)
                  a b c d e f
                  0 3 4 1 1 2 1
                  1 4 4 4 3 4 1
                  2 4 3 3 1 4 1
                  3 3 4 1 3 1 1
                  4 1 2 3 4 2 1
                  5 1 3 3 1 4 3

                  df.applymap(math.log10)

                  a b c d e f
                  0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
                  1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
                  2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
                  3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
                  4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
                  5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121



                  For the numpy solution, you could take the np.log10 of the dataframe, and reconstruct it as:



                  pd.DataFrame(np.log10(data), index=df.index, columns=df.columns)





                  share|improve this answer















                  From what it seems math.log10 cannot handle neither pandas dataframes nor ndarrays.



                  So one option would be to go with numpy, which also includes a function to compute the base 10 logarithm, np.log10, and reconstruct the dataframe as pointed out in other solutions.



                  Or if you want to go with math.log10, and the same would apply to other functions that cannot be directly vectorized, you can use DataFrame.applymap to apply math.log10 to the dataframe elementwise. Do note however that this solution will be slower than a vectorized approach using np.log10.




                  Use case



                  Here's an example of how this could be done using DataFrame.applymap:



                  df = pd.DataFrame(np.random.randint(1,5,(6,6)), columns=list('abcdef'))

                  print(df)
                  a b c d e f
                  0 3 4 1 1 2 1
                  1 4 4 4 3 4 1
                  2 4 3 3 1 4 1
                  3 3 4 1 3 1 1
                  4 1 2 3 4 2 1
                  5 1 3 3 1 4 3

                  df.applymap(math.log10)

                  a b c d e f
                  0 0.477121 0.602060 0.000000 0.000000 0.30103 0.000000
                  1 0.602060 0.602060 0.602060 0.477121 0.60206 0.000000
                  2 0.602060 0.477121 0.477121 0.000000 0.60206 0.000000
                  3 0.477121 0.602060 0.000000 0.477121 0.00000 0.000000
                  4 0.000000 0.301030 0.477121 0.602060 0.30103 0.000000
                  5 0.000000 0.477121 0.477121 0.000000 0.60206 0.477121



                  For the numpy solution, you could take the np.log10 of the dataframe, and reconstruct it as:



                  pd.DataFrame(np.log10(data), index=df.index, columns=df.columns)






                  share|improve this answer














                  share|improve this answer



                  share|improve this answer








                  edited 2 days ago

























                  answered 2 days ago









                  yatuyatu

                  12.7k31341




                  12.7k31341





















                      3














                      You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.



                      You can test it:



                      df.applymap(math.log10)





                      share|improve this answer





























                        3














                        You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.



                        You can test it:



                        df.applymap(math.log10)





                        share|improve this answer



























                          3












                          3








                          3







                          You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.



                          You can test it:



                          df.applymap(math.log10)





                          share|improve this answer















                          You may want to use the applymap method to apply math.log10 on the whole dataframe, here is the documentation.



                          You can test it:



                          df.applymap(math.log10)






                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited 2 days ago









                          IanS

                          8,56232763




                          8,56232763










                          answered 2 days ago









                          Valentin MercierValentin Mercier

                          9710




                          9710



























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