Pandas groupby with numpy array_split take to much time












0















I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.



Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.



Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.



def parallelize(data, func):
data_split = np.array_split(data, partitions)

pool = Pool(cores)
data = pd.concat(pool.map(func, data_split))
pool.close()
pool.join()

return data


You can se i'm trying to make multithread to handle my functions even faster.



when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.










share|improve this question













migrated from datascience.stackexchange.com Nov 21 '18 at 19:17


This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.























    0















    I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.



    Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.



    Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.



    def parallelize(data, func):
    data_split = np.array_split(data, partitions)

    pool = Pool(cores)
    data = pd.concat(pool.map(func, data_split))
    pool.close()
    pool.join()

    return data


    You can se i'm trying to make multithread to handle my functions even faster.



    when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.










    share|improve this question













    migrated from datascience.stackexchange.com Nov 21 '18 at 19:17


    This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.





















      0












      0








      0








      I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.



      Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.



      Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.



      def parallelize(data, func):
      data_split = np.array_split(data, partitions)

      pool = Pool(cores)
      data = pd.concat(pool.map(func, data_split))
      pool.close()
      pool.join()

      return data


      You can se i'm trying to make multithread to handle my functions even faster.



      when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.










      share|improve this question














      I'm working on to get a lots of rows out around 2.000.000 rows where i'm group it on a single columen.



      Its working fine so long, but when i'm in Python3 try to make a Numpy array_split its take for ever to load in, its take so long time i can't keep waiting on it when i testing.



      Normal if i'm not using groupby in Pandas its take few sec, but now when i'm using groupby('columen') i can't easy split my dataframe enymore.



      def parallelize(data, func):
      data_split = np.array_split(data, partitions)

      pool = Pool(cores)
      data = pd.concat(pool.map(func, data_split))
      pool.close()
      pool.join()

      return data


      You can se i'm trying to make multithread to handle my functions even faster.



      when i uncomment my groupby function in Pandas its working smooth, so its my groupby and then array_split there make troblese.







      python pandas numpy dataframe






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 21 '18 at 12:08









      ParisNakitaKejserParisNakitaKejser

      2,55783654




      2,55783654




      migrated from datascience.stackexchange.com Nov 21 '18 at 19:17


      This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.









      migrated from datascience.stackexchange.com Nov 21 '18 at 19:17


      This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.


























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