Python pandas read_csv for specfic records in columns












1















I am trying to import data from a large csv file 15GB+. I have to select few columns with specific values (there are over 50 columns) but as an example. I have used



df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', State'])


Is there a way where I can specify something like that:



df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', 'State'='abc'])


Can't find any option to do that






python-3.x







share|improve this question



























    1















    I am trying to import data from a large csv file 15GB+. I have to select few columns with specific values (there are over 50 columns) but as an example. I have used



    df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', State'])


    Is there a way where I can specify something like that:



    df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', 'State'='abc'])


    Can't find any option to do that






    python-3.x







    share|improve this question

























      1












      1








      1








      I am trying to import data from a large csv file 15GB+. I have to select few columns with specific values (there are over 50 columns) but as an example. I have used



      df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', State'])


      Is there a way where I can specify something like that:



      df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', 'State'='abc'])


      Can't find any option to do that






      python-3.x







      share|improve this question














      I am trying to import data from a large csv file 15GB+. I have to select few columns with specific values (there are over 50 columns) but as an example. I have used



      df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', State'])


      Is there a way where I can specify something like that:



      df=pd.read_csv('filename.csv', nrows=10000, usecols=['ID', 'State'='abc'])


      Can't find any option to do that






      python-3.x




      python-3.x






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 21 '18 at 1:37









      aus_fasaus_fas

      207




      207
























          2 Answers
          2






          active

          oldest

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          1














          There's no option to filter rows like that while reading csv files.
          What you can do is create an iterator then apply your filter to each chunk then concat the chunks. It would look something like:



          iterable = pd.read_csv('filename.csv', usecols=['ID', 'State'], iterator=True, chunksize=10000)
          
df = pd.concat([chunk[chunk['State'] == 'abc'] for chunk in iterable])





          share|improve this answer
























          • Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

            – aus_fas
            Nov 21 '18 at 6:10





















          0














          Assuming that the resulting DataFrame for a selection where 'State' == 'abc' is small enough to be accommodated in RAM, you could extract those from the csv as follows. df is the resultant DataFrame.



          import pandas as pd 
          

          
inPath = 'filename.csv'
          
chunkSize = 10000 #size of chunks relies on your available memory
          

          
tmpDf = pd.read_csv(inPath,chunksize=chunkSize, 
          
            usecols=['ID', 'State'])
          
for chunk in tmpDf:
          
    try:
          
        df
          
    except NameError:
          
        df = tmpDf[tmpDf['State'] == 'abc']
          
    else:
          
        df = pd.concat([df, tmpDf[tmpDf['State'] == 'abc']])





          share|improve this 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














            There's no option to filter rows like that while reading csv files.
            What you can do is create an iterator then apply your filter to each chunk then concat the chunks. It would look something like:



            iterable = pd.read_csv('filename.csv', usecols=['ID', 'State'], iterator=True, chunksize=10000)
            
df = pd.concat([chunk[chunk['State'] == 'abc'] for chunk in iterable])





            share|improve this answer
























            • Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

              – aus_fas
              Nov 21 '18 at 6:10


















            1














            There's no option to filter rows like that while reading csv files.
            What you can do is create an iterator then apply your filter to each chunk then concat the chunks. It would look something like:



            iterable = pd.read_csv('filename.csv', usecols=['ID', 'State'], iterator=True, chunksize=10000)
            
df = pd.concat([chunk[chunk['State'] == 'abc'] for chunk in iterable])





            share|improve this answer
























            • Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

              – aus_fas
              Nov 21 '18 at 6:10
















            1












            1








            1







            There's no option to filter rows like that while reading csv files.
            What you can do is create an iterator then apply your filter to each chunk then concat the chunks. It would look something like:



            iterable = pd.read_csv('filename.csv', usecols=['ID', 'State'], iterator=True, chunksize=10000)
            
df = pd.concat([chunk[chunk['State'] == 'abc'] for chunk in iterable])





            share|improve this answer













            There's no option to filter rows like that while reading csv files.
            What you can do is create an iterator then apply your filter to each chunk then concat the chunks. It would look something like:



            iterable = pd.read_csv('filename.csv', usecols=['ID', 'State'], iterator=True, chunksize=10000)
            
df = pd.concat([chunk[chunk['State'] == 'abc'] for chunk in iterable])






            share|improve this answer












            share|improve this answer



            share|improve this answer










            answered Nov 21 '18 at 1:44









            zrelovazrelova

            7181523




            7181523













            • Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

              – aus_fas
              Nov 21 '18 at 6:10





















            • Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

              – aus_fas
              Nov 21 '18 at 6:10



















            Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

            – aus_fas
            Nov 21 '18 at 6:10







            Worked for me. The execution time was 8min! How to find out the optimum chunk size ?

            – aus_fas
            Nov 21 '18 at 6:10















            0














            Assuming that the resulting DataFrame for a selection where 'State' == 'abc' is small enough to be accommodated in RAM, you could extract those from the csv as follows. df is the resultant DataFrame.



            import pandas as pd 
            

            
inPath = 'filename.csv'
            
chunkSize = 10000 #size of chunks relies on your available memory
            

            
tmpDf = pd.read_csv(inPath,chunksize=chunkSize, 
            
            usecols=['ID', 'State'])
            
for chunk in tmpDf:
            
    try:
            
        df
            
    except NameError:
            
        df = tmpDf[tmpDf['State'] == 'abc']
            
    else:
            
        df = pd.concat([df, tmpDf[tmpDf['State'] == 'abc']])





            share|improve this answer






























              0














              Assuming that the resulting DataFrame for a selection where 'State' == 'abc' is small enough to be accommodated in RAM, you could extract those from the csv as follows. df is the resultant DataFrame.



              import pandas as pd 
              

              
inPath = 'filename.csv'
              
chunkSize = 10000 #size of chunks relies on your available memory
              

              
tmpDf = pd.read_csv(inPath,chunksize=chunkSize, 
              
            usecols=['ID', 'State'])
              
for chunk in tmpDf:
              
    try:
              
        df
              
    except NameError:
              
        df = tmpDf[tmpDf['State'] == 'abc']
              
    else:
              
        df = pd.concat([df, tmpDf[tmpDf['State'] == 'abc']])





              share|improve this answer




























                0












                0








                0







                Assuming that the resulting DataFrame for a selection where 'State' == 'abc' is small enough to be accommodated in RAM, you could extract those from the csv as follows. df is the resultant DataFrame.



                import pandas as pd 
                

                
inPath = 'filename.csv'
                
chunkSize = 10000 #size of chunks relies on your available memory
                

                
tmpDf = pd.read_csv(inPath,chunksize=chunkSize, 
                
            usecols=['ID', 'State'])
                
for chunk in tmpDf:
                
    try:
                
        df
                
    except NameError:
                
        df = tmpDf[tmpDf['State'] == 'abc']
                
    else:
                
        df = pd.concat([df, tmpDf[tmpDf['State'] == 'abc']])





                share|improve this answer















                Assuming that the resulting DataFrame for a selection where 'State' == 'abc' is small enough to be accommodated in RAM, you could extract those from the csv as follows. df is the resultant DataFrame.



                import pandas as pd 
                

                
inPath = 'filename.csv'
                
chunkSize = 10000 #size of chunks relies on your available memory
                

                
tmpDf = pd.read_csv(inPath,chunksize=chunkSize, 
                
            usecols=['ID', 'State'])
                
for chunk in tmpDf:
                
    try:
                
        df
                
    except NameError:
                
        df = tmpDf[tmpDf['State'] == 'abc']
                
    else:
                
        df = pd.concat([df, tmpDf[tmpDf['State'] == 'abc']])






                share|improve this answer














                share|improve this answer



                share|improve this answer








                edited Nov 21 '18 at 2:32

























                answered Nov 21 '18 at 2:19









                Mark WarburtonMark Warburton

                9217




                9217






























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