How do I read a large csv file with pandas

Question

I am trying to read a large csv file  aprox  6 GB  in pandas and i am getting a memory error   MemoryError                               Traceback  most recent call last   lt ipython-input-58-67a72687871b gt  in  lt module gt    ---- gt  1 data pd read csv  aphro csv  sep            MemoryError     Any help on this

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In case someone is still looking for something like this  I found that this new library called modin can help  It uses distributed computing that can help with the read  Here s a nice article comparing its functionality with pandas  It essentially uses the same functions as pandas   import modin pandas as pd pd read csv CSV FILE NAME

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For large data l recommend you use the library  dask   e g      Dataframes implement the Pandas API import dask dataframe as dd df   dd read csv  s3       2018- -  csv     You can read more from the documentation here   Another great alternative would be to use modin because all the functionality is identical to pandas yet it leverages on distributed dataframe libraries such as dask

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Solution 1    Using pandas with large data  Solution 2   TextFileReader   pd read csv path  chunksize 1000     the number of rows per chunk  dfList      for df in TextFileReader      dfList append df   df   pd concat dfList sort False

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Before using chunksize option if you want to be sure about the process function that you want to write inside the chunking for-loop as mentioned by  unutbu you can simply use nrows option   small df   pd read csv filename  nrows 100    Once you are sure that the process block is ready  you can put that in the chunking for loop for the entire dataframe

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You can read in the data as chunks and save each chunk as pickle    import pandas as pd  import pickle  in path       Path where the large file is out path       Path to save the pickle files to chunk size   400000  size of chunks relies on your available memory separator        reader   pd read csv in path sep separator chunksize chunk size                       low memory False        for i  chunk in enumerate reader       out file   out path     data    pkl  format i 1      with open out file   wb   as f          pickle dump chunk f pickle HIGHEST PROTOCOL    In the next step you read in the pickles and append each pickle to your desired dataframe   import glob pickle path       Same Path as out path i e  where the pickle files are  data p files    for name in glob glob pickle path     data   pkl       data p files append name    df   pd DataFrame     for i in range len data p files        df   df append pd read pickle data p files i   ignore index True

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I want to make a more comprehensive answer based off of the most of the potential solutions that are already provided  I also want to point out one more potential aid that may help reading process   Option 1  dtypes   dtypes  is a pretty powerful parameter that you can use to reduce the memory pressure of read methods  See this and this answer  Pandas  on default  try to infer dtypes of the data   Referring to data structures  every data stored  a memory allocation takes place  At a basic level refer to the values below  The table below illustrates values for C programming language    The maximum value of UNSIGNED CHAR   255                                     The minimum value of SHORT INT   -32768                                      The maximum value of SHORT INT   32767                                       The minimum value of INT   -2147483648                                       The maximum value of INT   2147483647                                        The minimum value of CHAR   -128                                             The maximum value of CHAR   127                                              The minimum value of LONG   -9223372036854775808                             The maximum value of LONG   9223372036854775807   Refer to this page to see the matching between NumPy and C types   Let s say you have an array of integers of digits  You can both theoretically and practically assign  say array of 16-bit integer type  but you would then allocate more memory than you actually need to store that array  To prevent this  you can set dtype option on read csv  You do not want to store the array items as long integer where actually you can fit them with 8-bit integer  np int8 or np uint8    Observe the following dtype map    Source  https   pbpython com pandas dtypes html  You can pass dtype parameter as a parameter on pandas methods as dict on read like  column  type    import numpy as np import pandas as pd  df dtype              column 1   int           column 2   str           column 3   np int16           column 4   np uint8                       column n   np float32    df   pd read csv  path to file   dtype df dtype    Option 2  Read by Chunks  Reading the data in chunks allows you to access a part of the data in-memory  and you can apply preprocessing on your data and preserve the processed data rather than raw data  It d be much better if you combine this option with the first one  dtypes   I want to point out the pandas cookbook sections for that process  where you can find it here  Note those two sections there    Reading a csv chunk-by-chunk Reading only certain rows of a csv chunk-by-chunk   Option 3  Dask  Dask is a framework that is defined in Dask s website as      Dask provides advanced parallelism for analytics  enabling performance at scale for the tools you love   It was born to cover the necessary parts where pandas cannot reach  Dask is a powerful framework that allows you much more data access by processing it in a distributed way   You can use dask to preprocess your data as a whole  Dask takes care of the chunking part  so unlike pandas you can just define your processing steps and let Dask do the work  Dask does not apply the computations before it is explicitly pushed by compute and or persist  see the answer here for the difference    Other Aids  Ideas    ETL flow designed for the data  Keeping only what is needed from the raw data    First  apply ETL to whole data with frameworks like Dask or PySpark  and export the processed data  Then see if the processed data can be fit in the memory as a whole   Consider increasing your RAM  Consider working with that data on a cloud platform

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The function read csv and read table is almost the same  But you must assign the delimiter         when you use the function read table in your program   def get from action data fname  chunk size 100000       reader   pd read csv fname  header 0  iterator True      chunks          loop   True     while loop          try              chunk   reader get chunk chunk size    user id    type                chunks append chunk          except StopIteration              loop   False             print  Iteration is stopped        df ac   pd concat chunks  ignore index True

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Chunking shouldn t always be the first port of call for this problem   Is the file large due to repeated non-numeric data or unwanted columns  If so  you can sometimes see massive memory savings by reading in columns as categories and selecting required columns via pd read csv usecols parameter   Does your workflow require slicing  manipulating  exporting  If so  you can use dask dataframe to slice  perform your calculations and export iteratively  Chunking is performed silently by dask  which also supports a subset of pandas API   If all else fails  read line by line via chunks  Chunk via pandas or via csv library as a last resort

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In addition to the answers above  for those who want to process CSV and then export to csv  parquet or SQL  d6tstack is another good option  You can load multiple files and it deals with data schema changes  added removed columns   Chunked out of core support is already built in   def apply dfg         do stuff     return dfg  c   d6tstack combine csv CombinerCSV  bigfile csv   apply after read apply  sep      chunksize 1e6     or c   d6tstack combine csv CombinerCSV glob glob    csv    apply after read apply  chunksize 1e6     output to various formats  automatically chunked to reduce memory consumption c to csv combine filename  out csv   c to parquet combine filename  out pq   c to psql combine  postgresql psycopg2   usr pwd localhost db    tablename     fast for postgres c to mysql combine  mysql mysqlconnector   usr pwd localhost db    tablename     fast for mysql c to sql combine  postgresql psycopg2   usr pwd localhost db    tablename     slow but flexible

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Here follows an example   chunkTemp      queryTemp      query   pd DataFrame    for chunk in pd read csv file  header 0  chunksize  lt your chunksize gt   iterator True  low memory False         REPLACING BLANK SPACES AT COLUMNS  NAMES FOR SQL OPTIMIZATION     chunk   chunk rename columns    c  c replace          for c in chunk columns         YOU CAN EITHER        1 BUFFER THE CHUNKS IN ORDER TO LOAD YOUR WHOLE DATASET      chunkTemp append chunk        2 DO YOUR PROCESSING OVER A CHUNK AND STORE THE RESULT OF IT     query   chunk chunk  lt column name gt   str startswith  lt some pattern gt            BUFFERING PROCESSED DATA     queryTemp append query       NEVER DO pd concat OR pd DataFrame   INSIDE A LOOP print  Database  CONCATENATING CHUNKS INTO A SINGLE DATAFRAME   chunk   pd concat chunkTemp  print  Database  LOADED     CONCATENATING PROCESSED DATA query   pd concat queryTemp  print query

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The error shows that the machine does not have enough memory to read the entire CSV into a DataFrame at one time  Assuming you do not need the entire dataset in memory all at one time  one way to avoid the problem would be to process the CSV in chunks  by specifying the chunksize parameter   chunksize   10    6 for chunk in pd read csv filename  chunksize chunksize       process chunk   The chunksize parameter specifies the number of rows per chunk   The last chunk may contain fewer than chunksize rows  of course    pandas  gt   1 2 read csv with chunksize returns a context manager  to be used like so  chunksize   10    6 with pd read csv filename  chunksize chunksize  as reader      for chunk in reader          process chunk   See GH38225

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If you use pandas read large file into chunk and then yield row by row  here is what I have done  import pandas as pd  def chunck generator filename  header False chunk size   10    5      for chunk in pd read csv filename delimiter      iterator True  chunksize chunk size  parse dates  1              yield  chunk   def  generator  filename  header False chunk size   10    5       chunk   chunck generator filename  header False chunk size   10    5      for row in chunk          yield row  if   name         main     filename   r file csv          generator   generator filename filename          while True             print next generator

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You can try sframe  that have the same syntax as pandas but allows you to manipulate files that are bigger than your RAM

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The above answer is already satisfying the topic  Anyway  if you need all the data in memory - have a look at bcolz  Its compressing the data in memory  I have had really good experience with it  But its missing a lot of pandas features  Edit  I got compression rates at around 1 10 or orig size i think  of course depending of the kind of data  Important features missing were aggregates

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I proceeded like this   chunks pd read table  aphro csv  chunksize 1000000 sep              names   lat   long   rf   date   slno   index col  slno           header None parse dates   date     df pd DataFrame    time df pd concat chunk groupby   lat   long  chunk  date   map lambda x  x year     rf   agg   sum    for chunk in chunks

[python] How do I read a large csv file with pandas?

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