Multiprocessing a for loop in Time series





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I want to use multiprocessing for a 'For loop'. So basically from a another dataframe I am passing ID,PID to main dataframe, so depends on ID,PID it will filter the dataframe & build a model. I tried, but it taking same time.



Code:



from time import time
import defs
import multiprocessing

if __name__ == '__main__':
currtime = time()
test =
for index,row in temp_sub.iterrows():
local_id = row["ID"]
local_pid = row["PID"]
p = multiprocessing.Pool(multiprocessing.cpu_count())
df = p.apply(defs.framing_df,(df,local_id,local_pid))
test.append(df)
print ('parallel: time elapsed:', time() - currtime)


#temp_sub : small dataframe consists of ids & pids
#df: main dataframe
#defs: wrote a framing_df function & saved in defs.py file


Where I am making mistake, please let me know. It is working fine, but taking same time as sequential process.










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    $begingroup$


    I want to use multiprocessing for a 'For loop'. So basically from a another dataframe I am passing ID,PID to main dataframe, so depends on ID,PID it will filter the dataframe & build a model. I tried, but it taking same time.



    Code:



    from time import time
    import defs
    import multiprocessing

    if __name__ == '__main__':
    currtime = time()
    test =
    for index,row in temp_sub.iterrows():
    local_id = row["ID"]
    local_pid = row["PID"]
    p = multiprocessing.Pool(multiprocessing.cpu_count())
    df = p.apply(defs.framing_df,(df,local_id,local_pid))
    test.append(df)
    print ('parallel: time elapsed:', time() - currtime)


    #temp_sub : small dataframe consists of ids & pids
    #df: main dataframe
    #defs: wrote a framing_df function & saved in defs.py file


    Where I am making mistake, please let me know. It is working fine, but taking same time as sequential process.










    share|improve this question







    New contributor




    A14 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I want to use multiprocessing for a 'For loop'. So basically from a another dataframe I am passing ID,PID to main dataframe, so depends on ID,PID it will filter the dataframe & build a model. I tried, but it taking same time.



      Code:



      from time import time
      import defs
      import multiprocessing

      if __name__ == '__main__':
      currtime = time()
      test =
      for index,row in temp_sub.iterrows():
      local_id = row["ID"]
      local_pid = row["PID"]
      p = multiprocessing.Pool(multiprocessing.cpu_count())
      df = p.apply(defs.framing_df,(df,local_id,local_pid))
      test.append(df)
      print ('parallel: time elapsed:', time() - currtime)


      #temp_sub : small dataframe consists of ids & pids
      #df: main dataframe
      #defs: wrote a framing_df function & saved in defs.py file


      Where I am making mistake, please let me know. It is working fine, but taking same time as sequential process.










      share|improve this question







      New contributor




      A14 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I want to use multiprocessing for a 'For loop'. So basically from a another dataframe I am passing ID,PID to main dataframe, so depends on ID,PID it will filter the dataframe & build a model. I tried, but it taking same time.



      Code:



      from time import time
      import defs
      import multiprocessing

      if __name__ == '__main__':
      currtime = time()
      test =
      for index,row in temp_sub.iterrows():
      local_id = row["ID"]
      local_pid = row["PID"]
      p = multiprocessing.Pool(multiprocessing.cpu_count())
      df = p.apply(defs.framing_df,(df,local_id,local_pid))
      test.append(df)
      print ('parallel: time elapsed:', time() - currtime)


      #temp_sub : small dataframe consists of ids & pids
      #df: main dataframe
      #defs: wrote a framing_df function & saved in defs.py file


      Where I am making mistake, please let me know. It is working fine, but taking same time as sequential process.







      python-3.x multiprocessing






      share|improve this question







      New contributor




      A14 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 question







      New contributor




      A14 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 question




      share|improve this question






      New contributor




      A14 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 23 mins ago









      A14A14

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      New contributor





      A14 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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