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Dask compute gives AttributeError: 'Series' object has no attribute 'encode'


How to know if an object has an attribute in Python“Large data” work flows using pandasCollecting attributes from dask dataframe providersConvert Python Dask Series to list or Dask DataFrame inside for loopDask method on dataframe to return a dictionary of applied method resultsComputing in-place with daskpython dask dataframes - concatenate groupby.apply output to a single data frameHow to efficiently parallelize time series forecasting using dask?Dask AttributeError: 'DataFrame' object has no attribute '_repr_data'Dask - How to concatenate Series into a DataFrame with apply?






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0















I would like to apply a function to each row of a dask dataframe.



Executing the operation with ddf.compute() gives me an error:



AttributeError: 'Series' object has no attribute 'encode'


This is my code:



def polar(data):
data=scale(sid.polarity_scores(data.tweet)['compound'])
return data

t_data['sentiment'] = t_data.map_partitions(polar, meta=('sentiment', int))


And using t_data.head() also result in same error.










share|improve this question






























    0















    I would like to apply a function to each row of a dask dataframe.



    Executing the operation with ddf.compute() gives me an error:



    AttributeError: 'Series' object has no attribute 'encode'


    This is my code:



    def polar(data):
    data=scale(sid.polarity_scores(data.tweet)['compound'])
    return data

    t_data['sentiment'] = t_data.map_partitions(polar, meta=('sentiment', int))


    And using t_data.head() also result in same error.










    share|improve this question


























      0












      0








      0








      I would like to apply a function to each row of a dask dataframe.



      Executing the operation with ddf.compute() gives me an error:



      AttributeError: 'Series' object has no attribute 'encode'


      This is my code:



      def polar(data):
      data=scale(sid.polarity_scores(data.tweet)['compound'])
      return data

      t_data['sentiment'] = t_data.map_partitions(polar, meta=('sentiment', int))


      And using t_data.head() also result in same error.










      share|improve this question
















      I would like to apply a function to each row of a dask dataframe.



      Executing the operation with ddf.compute() gives me an error:



      AttributeError: 'Series' object has no attribute 'encode'


      This is my code:



      def polar(data):
      data=scale(sid.polarity_scores(data.tweet)['compound'])
      return data

      t_data['sentiment'] = t_data.map_partitions(polar, meta=('sentiment', int))


      And using t_data.head() also result in same error.







      python dataframe dask






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 9 at 3:19









      Christian Dean

      15.7k62859




      15.7k62859










      asked Sep 28 '18 at 9:16









      DICER45DICER45

      529




      529






















          2 Answers
          2






          active

          oldest

          votes


















          1














          I have found out the answer. You have to apply for partition.



          t_data['sentiment']=t_data.map_partitions(lambda df : df.apply(polar,axis=1))





          share|improve this answer






























            0














            You can use the following:



            t_data.apply(polar, axis=1)





            share|improve this answer























              Your Answer






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              2 Answers
              2






              active

              oldest

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              2 Answers
              2






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              1














              I have found out the answer. You have to apply for partition.



              t_data['sentiment']=t_data.map_partitions(lambda df : df.apply(polar,axis=1))





              share|improve this answer



























                1














                I have found out the answer. You have to apply for partition.



                t_data['sentiment']=t_data.map_partitions(lambda df : df.apply(polar,axis=1))





                share|improve this answer

























                  1












                  1








                  1







                  I have found out the answer. You have to apply for partition.



                  t_data['sentiment']=t_data.map_partitions(lambda df : df.apply(polar,axis=1))





                  share|improve this answer













                  I have found out the answer. You have to apply for partition.



                  t_data['sentiment']=t_data.map_partitions(lambda df : df.apply(polar,axis=1))






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Sep 28 '18 at 11:02









                  DICER45DICER45

                  529




                  529























                      0














                      You can use the following:



                      t_data.apply(polar, axis=1)





                      share|improve this answer



























                        0














                        You can use the following:



                        t_data.apply(polar, axis=1)





                        share|improve this answer

























                          0












                          0








                          0







                          You can use the following:



                          t_data.apply(polar, axis=1)





                          share|improve this answer













                          You can use the following:



                          t_data.apply(polar, axis=1)






                          share|improve this answer












                          share|improve this answer



                          share|improve this answer










                          answered Sep 28 '18 at 12:22









                          MRocklinMRocklin

                          27.3k1473131




                          27.3k1473131



























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