Problem with python memory, flush, csv size












0















After solving a sorting of a dataset, I have a problem at this point of my code.



with open(fns_land[xx]) as infile:

    lines = infile.readlines()

    for line in lines:

        result_station.append(line.split(',')[0])

        result_date.append(line.split(',')[1])

        result_metar.append(line.split(',')[-1])


I have a problem in the lines line. In this line the data are sometimes to huge and i get a kill error.



Is there a short/nice way to rewrite this point?






python arrays memory flush







share|improve this question

























  • Possible duplicate of Python readlines() usage and efficient practice for reading

    – The Pjot
    Nov 14 '18 at 14:23
















0















After solving a sorting of a dataset, I have a problem at this point of my code.



with open(fns_land[xx]) as infile:

    lines = infile.readlines()

    for line in lines:

        result_station.append(line.split(',')[0])

        result_date.append(line.split(',')[1])

        result_metar.append(line.split(',')[-1])


I have a problem in the lines line. In this line the data are sometimes to huge and i get a kill error.



Is there a short/nice way to rewrite this point?






python arrays memory flush







share|improve this question

























  • Possible duplicate of Python readlines() usage and efficient practice for reading

    – The Pjot
    Nov 14 '18 at 14:23














0












0








0








After solving a sorting of a dataset, I have a problem at this point of my code.



with open(fns_land[xx]) as infile:

    lines = infile.readlines()

    for line in lines:

        result_station.append(line.split(',')[0])

        result_date.append(line.split(',')[1])

        result_metar.append(line.split(',')[-1])


I have a problem in the lines line. In this line the data are sometimes to huge and i get a kill error.



Is there a short/nice way to rewrite this point?






python arrays memory flush







share|improve this question
















After solving a sorting of a dataset, I have a problem at this point of my code.



with open(fns_land[xx]) as infile:

    lines = infile.readlines()

    for line in lines:

        result_station.append(line.split(',')[0])

        result_date.append(line.split(',')[1])

        result_metar.append(line.split(',')[-1])


I have a problem in the lines line. In this line the data are sometimes to huge and i get a kill error.



Is there a short/nice way to rewrite this point?






python arrays memory flush




python arrays memory flush






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 14 '18 at 15:56









toti08

1,77941623




1,77941623










asked Nov 14 '18 at 14:17









S.KociokS.Kociok

287




287













  • Possible duplicate of Python readlines() usage and efficient practice for reading

    – The Pjot
    Nov 14 '18 at 14:23



















  • Possible duplicate of Python readlines() usage and efficient practice for reading

    – The Pjot
    Nov 14 '18 at 14:23

















Possible duplicate of Python readlines() usage and efficient practice for reading

– The Pjot
Nov 14 '18 at 14:23





Possible duplicate of Python readlines() usage and efficient practice for reading

– The Pjot
Nov 14 '18 at 14:23












2 Answers
2






active

oldest

votes


















1














Use readline instead, this read it one line at a time without loading the entire file into memory.



with open(fns_land[xx]) as infile:

    while True:

        line = infile.readline()

        if not line:

            break

        result_station.append(line.split(',')[0])

        result_date.append(line.split(',')[1])

        result_metar.append(line.split(',')[-1])





share|improve this answer































    1














    If you are dealing with a dataset, I would suggest that you have a look at pandas, which I great for dealing with data wrangling.



    If your problem is a large dataset, you could load the data in chunks.



    import pandas as pd
    
tfr = pd.read_csv('fns_land{0}.csv'.format(xx), iterator=True, chunksize=1000)



    1. Line: imported pandas modul

    2. Line: read data from your csv file in chunks of 1000 lines.


    This will be of type pandas.io.parsers.TextFileReader. To load the entire csv file, you follow up with:



    df = pd.concat(tfr, ignore_index=True)


    The parameter ignore_index=True is added to avoid duplicity of indexes.



    You now have all your data loaded into a dataframe. Then do your data manipulation on the columns as vectors, which also is faster than regular line by line.



    Have a look here this question that dealt with something similar.






    share|improve this answer
























    • Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

      – S.Kociok
      Nov 14 '18 at 14:54











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    2 Answers
    2






    active

    oldest

    votes








    2 Answers
    2






    active

    oldest

    votes









    active

    oldest

    votes






    active

    oldest

    votes









    1














    Use readline instead, this read it one line at a time without loading the entire file into memory.



    with open(fns_land[xx]) as infile:
    
    while True:
    
        line = infile.readline()
    
        if not line:
    
            break
    
        result_station.append(line.split(',')[0])
    
        result_date.append(line.split(',')[1])
    
        result_metar.append(line.split(',')[-1])





    share|improve this answer




























      1














      Use readline instead, this read it one line at a time without loading the entire file into memory.



      with open(fns_land[xx]) as infile:
      
    while True:
      
        line = infile.readline()
      
        if not line:
      
            break
      
        result_station.append(line.split(',')[0])
      
        result_date.append(line.split(',')[1])
      
        result_metar.append(line.split(',')[-1])





      share|improve this answer


























        1












        1








        1







        Use readline instead, this read it one line at a time without loading the entire file into memory.



        with open(fns_land[xx]) as infile:
        
    while True:
        
        line = infile.readline()
        
        if not line:
        
            break
        
        result_station.append(line.split(',')[0])
        
        result_date.append(line.split(',')[1])
        
        result_metar.append(line.split(',')[-1])





        share|improve this answer













        Use readline instead, this read it one line at a time without loading the entire file into memory.



        with open(fns_land[xx]) as infile:
        
    while True:
        
        line = infile.readline()
        
        if not line:
        
            break
        
        result_station.append(line.split(',')[0])
        
        result_date.append(line.split(',')[1])
        
        result_metar.append(line.split(',')[-1])






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 14 '18 at 14:24









        Rocky LiRocky Li

        3,1131516




        3,1131516

























            1














            If you are dealing with a dataset, I would suggest that you have a look at pandas, which I great for dealing with data wrangling.



            If your problem is a large dataset, you could load the data in chunks.



            import pandas as pd
            
tfr = pd.read_csv('fns_land{0}.csv'.format(xx), iterator=True, chunksize=1000)



            1. Line: imported pandas modul

            2. Line: read data from your csv file in chunks of 1000 lines.


            This will be of type pandas.io.parsers.TextFileReader. To load the entire csv file, you follow up with:



            df = pd.concat(tfr, ignore_index=True)


            The parameter ignore_index=True is added to avoid duplicity of indexes.



            You now have all your data loaded into a dataframe. Then do your data manipulation on the columns as vectors, which also is faster than regular line by line.



            Have a look here this question that dealt with something similar.






            share|improve this answer
























            • Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

              – S.Kociok
              Nov 14 '18 at 14:54
















            1














            If you are dealing with a dataset, I would suggest that you have a look at pandas, which I great for dealing with data wrangling.



            If your problem is a large dataset, you could load the data in chunks.



            import pandas as pd
            
tfr = pd.read_csv('fns_land{0}.csv'.format(xx), iterator=True, chunksize=1000)



            1. Line: imported pandas modul

            2. Line: read data from your csv file in chunks of 1000 lines.


            This will be of type pandas.io.parsers.TextFileReader. To load the entire csv file, you follow up with:



            df = pd.concat(tfr, ignore_index=True)


            The parameter ignore_index=True is added to avoid duplicity of indexes.



            You now have all your data loaded into a dataframe. Then do your data manipulation on the columns as vectors, which also is faster than regular line by line.



            Have a look here this question that dealt with something similar.






            share|improve this answer
























            • Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

              – S.Kociok
              Nov 14 '18 at 14:54














            1












            1








            1







            If you are dealing with a dataset, I would suggest that you have a look at pandas, which I great for dealing with data wrangling.



            If your problem is a large dataset, you could load the data in chunks.



            import pandas as pd
            
tfr = pd.read_csv('fns_land{0}.csv'.format(xx), iterator=True, chunksize=1000)



            1. Line: imported pandas modul

            2. Line: read data from your csv file in chunks of 1000 lines.


            This will be of type pandas.io.parsers.TextFileReader. To load the entire csv file, you follow up with:



            df = pd.concat(tfr, ignore_index=True)


            The parameter ignore_index=True is added to avoid duplicity of indexes.



            You now have all your data loaded into a dataframe. Then do your data manipulation on the columns as vectors, which also is faster than regular line by line.



            Have a look here this question that dealt with something similar.






            share|improve this answer













            If you are dealing with a dataset, I would suggest that you have a look at pandas, which I great for dealing with data wrangling.



            If your problem is a large dataset, you could load the data in chunks.



            import pandas as pd
            
tfr = pd.read_csv('fns_land{0}.csv'.format(xx), iterator=True, chunksize=1000)



            1. Line: imported pandas modul

            2. Line: read data from your csv file in chunks of 1000 lines.


            This will be of type pandas.io.parsers.TextFileReader. To load the entire csv file, you follow up with:



            df = pd.concat(tfr, ignore_index=True)


            The parameter ignore_index=True is added to avoid duplicity of indexes.



            You now have all your data loaded into a dataframe. Then do your data manipulation on the columns as vectors, which also is faster than regular line by line.



            Have a look here this question that dealt with something similar.







            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered Nov 14 '18 at 14:41









            PhilipPhilip

            341212




            341212













            • Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

              – S.Kociok
              Nov 14 '18 at 14:54



















            • Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

              – S.Kociok
              Nov 14 '18 at 14:54

















            Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

            – S.Kociok
            Nov 14 '18 at 14:54





            Thanks. But for my using the open methode was the best way. I only want to read in three colums out of 1000 colums. For the next time it is maybe a better way with pandas.

            – S.Kociok
            Nov 14 '18 at 14:54


















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