How to get line count of a large file cheaply in Python

Question

I need to get a line count of a large file  hundreds of thousands of lines  in python  What is the most efficient way both memory- and time-wise   At the moment I do   def file len fname       with open fname  as f          for i  l in enumerate f               pass     return i   1   is it possible to do any better

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This is the fastest thing I have found using pure python. You can use whatever amount of memory you want by setting buffer, though 2**16 appears to be a sweet spot on my computer.

from functools import partial

buffer=2**16
with open(myfile) as f:
        print sum(x.count('\n') for x in iter(partial(f.read,buffer), ''))

I found the answer here Why is reading lines from stdin much slower in C++ than Python? and tweaked it just a tiny bit. Its a very good read to understand how to count lines quickly, though wc -l is still about 75% faster than anything else.

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print open  file txt    r   read   count   n     1

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What about this  def file len fname     counts   itertools count     with open fname  as f       for   in f  counts next     return counts next

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You can t get any better than that   After all  any solution will have to read the entire file  figure out how many  n you have  and return that result   Do you have a better way of doing that without reading the entire file  Not sure    The best solution will always be I O-bound  best you can do is make sure you don t use unnecessary memory  but it looks like you have that covered

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def count text file lines path       with open path   rt   as file          line count   sum 1 for  line in file      return line count

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Why not read the first 100 and the last 100 lines and estimate the average line length  then divide the total file size through that numbers  If you don t need a exact value this could work

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count   max enumerate open filename    0

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I got a small  4-8   improvement with this version which re-uses a constant buffer so it should avoid any memory or GC overhead   lines   0 buffer   bytearray 2048  with open filename  as f    while f readinto buffer   gt  0        lines    buffer count   n     You can play around with the buffer size and maybe see a little improvement

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One line solution   import os os system  wc -l  filename       My snippet    gt  gt  gt  os system  wc -l   txt    0 bar txt 1000 command txt 3 test file txt 1003 total

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Create an executable script file named count py       usr bin python  import sys count   0 for line in sys stdin      count  1   And then pipe the file s content into the python script  cat huge txt     count py  Pipe works also on Powershell  so you will end up counting number of lines   For me  on Linux it was 30  faster than the naive solution   count 1 with open  huge txt   as f      count  1

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def file len full path         Count number of lines in a file       f   open full path    nr of lines   sum 1 for line in f    f close     return nr of lines

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Here is what I use  seems pretty clean   import subprocess  def count file lines file path               Counts the number of lines in a file using wc utility       param file path  path to file      return  int  no of lines             num   subprocess check output   wc    -l   file path       num   num split          return int num 0     UPDATE  This is marginally faster than using pure python but at the cost of memory usage  Subprocess will fork a new process with the same memory footprint as the parent process while it executes your command

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what about this   import sys sys stdin open  fname   r   data sys stdin readlines   print  counted  len data   lines

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I have modified the buffer case like this   def CountLines filename       f   open filename      try          lines   1         buf size   1024   1024         read f   f read   loop optimization         buf   read f buf size             Empty file         if not buf              return 0          while buf              lines    buf count   n               buf   read f buf size           return lines     finally          f close     Now also empty files and the last line  without  n  are counted

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If one wants to get the line count cheaply in Python in Linux  I recommend this method   import os print os popen  wc -l file path   readline   split   0    file path can be both abstract file path or relative path  Hope this may help

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I believe that a memory mapped file will be the fastest solution  I tried four functions  the function posted by the OP  opcount   a simple iteration over the lines in the file  simplecount   readline with a memory-mapped filed  mmap   mapcount   and the buffer read solution offered by Mykola Kharechko  bufcount    I ran each function five times  and calculated the average run-time for a 1 2 million-line text file   Windows XP  Python 2 5  2GB RAM  2 GHz AMD processor  Here are my results   mapcount   0 465599966049 simplecount   0 756399965286 bufcount   0 546800041199 opcount   0 718600034714   Edit  numbers for Python 2 6   mapcount   0 471799945831 simplecount   0 634400033951 bufcount   0 468800067902 opcount   0 602999973297   So the buffer read strategy seems to be the fastest for Windows Python 2 6  Here is the code   from   future   import with statement import time import mmap import random from collections import defaultdict  def mapcount filename       f   open filename   r        buf   mmap mmap f fileno    0      lines   0     readline   buf readline     while readline            lines    1     return lines  def simplecount filename       lines   0     for line in open filename           lines    1     return lines  def bufcount filename       f   open filename                        lines   0     buf size   1024   1024     read f   f read   loop optimization      buf   read f buf size      while buf          lines    buf count   n           buf   read f buf size       return lines  def opcount fname       with open fname  as f          for i  l in enumerate f               pass     return i   1   counts   defaultdict list   for i in range 5       for func in  mapcount  simplecount  bufcount  opcount           start time   time time           assert func  big file txt      1209138         counts func  append time time   - start time   for key  vals in counts items        print key   name         sum vals    float len vals

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Why wouldn t the following work   import sys    input comes from STDIN file   sys stdin data   file readlines      get total number of lines in file lines   len data   print lines   In this case  the len function uses the input lines as a means of determining the length

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Using Numba We can use Numba to JIT  Just in time  compile our function to machine code  def numbacountparallel fname  runs 2 8x faster than def file len fname  from the question  Notes  The OS had already cached the file to memory before the benchmarks were run as I don t see much disk activity on my PC  The time would be much slower when reading the file for the first time making the time advantage of using Numba insignificant  The JIT compilation takes extra time the first time the function is called  This would be useful if we were doing more than just counting lines  Cython is another option  http   numba pydata org  Conclusion As counting lines will be IO bound  use the def file len fname  from the question unless you want to do more than just count lines  import timeit  from numba import jit  prange import numpy as np  from itertools import  takewhile repeat   FILE       data us confirmed csv    40 6MB  371755 line file CR   ord   n       Copied from the question above  Used as a benchmark def file len fname       with open fname  as f          for i  l in enumerate f               pass     return i   1     Copied from another answer  Used as a benchmark def rawincount filename       f   open filename   rb       bufgen   takewhile lambda x  x   f read 1024 1024 10  for   in repeat None        return sum  buf count b  n   for buf in bufgen       Single thread  jit nopython True  def numbacountsingle chunk bs        c   0     for i in range len bs            if bs i     CR              c    1      return c   def numbacountsingle filename       f   open filename   quot rb quot       total   0     while True          chunk   f read 1024 1024 10          lines   numbacountsingle chunk chunk          total    lines         if not chunk              break      return total     Multi thread  jit nopython True  parallel True  def numbacountparallel chunk bs        c   0     for i in prange len bs            if bs i     CR              c    1      return c   def numbacountparallel filename       f   open filename   quot rb quot       total   0     while True          chunk   f read 1024 1024 10          lines   numbacountparallel chunk np frombuffer chunk  dtype np uint8           total    lines         if not chunk              break      return total  print  numbacountparallel   print numbacountparallel FILE     This allows Numba to compile and cache the function without adding to the time  print timeit Timer lambda  numbacountparallel FILE   timeit number 100    print   nnumbacountsingle   print numbacountsingle FILE   print timeit Timer lambda  numbacountsingle FILE   timeit number 100    print   nfile len   print file len FILE   print timeit Timer lambda  rawincount FILE   timeit number 100    print   nrawincount   print rawincount FILE   print timeit Timer lambda  rawincount FILE   timeit number 100    Time in seconds for 100 calls to each function numbacountparallel 371755 2 8007332000000003  numbacountsingle 371755 3 1508585999999994  file len 371755 6 7945494  rawincount 371755 6 815438

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How about this one-liner   file length   len open  myfile txt   r   read   split   n      Takes 0 003 sec using this method to time it on a 3900 line file  def c      import time   s   time time     file length   len open  myfile txt   r   read   split   n      print time time   - s

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If the file can fit into memory  then  with open fname  as f      count   len f read   split b  n    - 1

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Simple method   1    gt  gt  gt  f   len open  myfile txt   readlines     gt  gt  gt  f  430   2    gt  gt  gt  f   open  myfile txt   read   count   n    gt  gt  gt  f 430  gt  gt  gt    3   num lines   len list open  myfile txt

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the result of opening a file is an iterator  which can be converted to a sequence  which has a length   with open filename  as f     return len list f     this is more concise than your explicit loop  and avoids the enumerate

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I had to post this on a similar question until my reputation score jumped a bit  thanks to whoever bumped me      All of these solutions ignore one way to make this run considerably faster  namely by using the unbuffered  raw  interface  using bytearrays  and doing your own buffering   This only applies in Python 3   In Python 2  the raw interface may or may not be used by default  but in Python 3  you ll default into Unicode    Using a modified version of the timing tool  I believe the following code is faster  and marginally more pythonic  than any of the solutions offered   def rawcount filename       f   open filename   rb       lines   0     buf size   1024   1024     read f   f raw read      buf   read f buf size      while buf          lines    buf count b  n           buf   read f buf size       return lines   Using a separate generator function  this runs a smidge faster   def  make gen reader       b   reader 1024   1024      while b          yield b         b   reader 1024 1024   def rawgencount filename       f   open filename   rb       f gen    make gen f raw read      return sum  buf count b  n   for buf in f gen     This can be done completely with generators expressions in-line using itertools  but it gets pretty weird looking   from itertools import  takewhile repeat   def rawincount filename       f   open filename   rb       bufgen   takewhile lambda x  x   f raw read 1024 1024  for   in repeat None        return sum  buf count b  n   for buf in bufgen     Here are my timings   function      average  s  min  s   ratio rawincount        0 0043  0 0041   1 00 rawgencount       0 0044  0 0042   1 01 rawcount          0 0048  0 0045   1 09 bufcount          0 008   0 0068   1 64 wccount           0 01    0 0097   2 35 itercount         0 014   0 014    3 41 opcount           0 02    0 02     4 83 kylecount         0 021   0 021    5 05 simplecount       0 022   0 022    5 25 mapcount          0 037   0 031    7 46

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As for me this variant will be the fastest      usr bin env python  def main        f   open  filename                         lines   0     buf size   1024   1024     read f   f read   loop optimization      buf   read f buf size      while buf          lines    buf count   n           buf   read f buf size       print lines  if   name         main         main     reasons  buffering faster than reading line by line and string count is also very fast

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One line  probably pretty fast   num lines   sum 1 for line in open  myfile txt

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This code is shorter and clearer  It s probably the best way   num lines   open  yourfile ext   read   count   n

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You could execute a subprocess and run wc -l filename  import subprocess  def file len fname       p   subprocess Popen   wc    -l   fname   stdout subprocess PIPE                                                 stderr subprocess PIPE      result  err   p communicate       if p returncode    0          raise IOError err      return int result strip   split   0

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I would use Python s file object method readlines  as follows   with open input file  as foo      lines   len foo readlines      This opens the file  creates a list of lines in the file  counts the length of the list  saves that to a variable and closes the file again

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Kyle s answer   num lines   sum 1 for line in open  my file txt      is probably best  an alternative for this is  num lines    len open  my file txt   read   splitlines      Here is the comparision of performance of both   In  20   timeit sum 1 for line in open  Charts ipynb    100000 loops  best of 3  9 79   s per loop  In  21   timeit len open  Charts ipynb   read   splitlines    100000 loops  best of 3  12   s per loop

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def line count path       count   0     with open path  as lines          for count  l in enumerate lines  start 1               pass     return count

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Another possibility    import subprocess  def num lines in file fpath       return int subprocess check output  wc -l  s    fpath  shell True  strip   split   0

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If all the lines in your file are the same length  and contain only ASCII characters    you can do the following very cheaply   fileSize       os path getsize  pathToFile      file size in bytes bytesPerLine   someInteger                      don t forget to account for the newline character numLines       fileSize    bytesPerLine    I suspect more effort would be required to determine the number of bytes in a line if unicode characters like    are used

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Here is a python program to use the multiprocessing library to distribute the line counting across machines cores   My test improves counting a 20million line file from 26 seconds to 7 seconds using an 8 core windows 64 server   Note  not using memory mapping makes things much slower   import multiprocessing  sys  time  os  mmap import logging  logging handlers  def init logger pid       console format    P 0    levelname s   message s  format pid      logger   logging getLogger      New logger at root level     logger setLevel  logging INFO       logger handlers append  logging StreamHandler         logger handlers 0  setFormatter  logging Formatter  console format    d  m  y  H  M  S       def getFileLineCount  queues  pid  processes  file1        init logger pid      logging info   start         physical file   open file1   r          mmap mmap fileno  length   tagname   access   offset         m1   mmap mmap  physical file fileno    0  access mmap ACCESS READ         work out file size to divide up line counting      fSize   os stat file1  st size     chunk    fSize   processes    1      lines   0       get where I start and stop      seedStart   chunk    pid       seekEnd   chunk    pid 1      seekStart   int  seedStart      seekEnd   int  seekEnd       if seekEnd  lt  int  seekEnd   1           seekEnd    1      if  seedStart  lt  int seekStart   1           seekStart    1      if seekEnd  gt  fSize          seekEnd   fSize       find where to start     if pid  gt  0          m1 seek  seekStart            read next line         l1   m1 readline      need to use readline with memory mapped files         seekStart   m1 tell         tell previous rank my seek start to make their seek end      if pid  gt  0          queues pid-1  put  seekStart       if pid  lt  processes-1          seekEnd   queues pid  get        m1 seek  seekStart       l1   m1 readline        while len l1   gt  0          lines    1         l1   m1 readline           if m1 tell    gt  seekEnd or len l1     0              break      logging info   done          add up the results     if pid    0          for p in range 1 processes               lines    queues 0  get           queues 0  put lines    the total lines counted     else          queues 0  put lines       m1 close       physical file close    if   name         main         init logger   main        if len sys argv   gt  1          file name   sys argv 1      else          logging fatal   parameters required  file-name  processes             exit        t   time time       processes   multiprocessing cpu count       if len sys argv   gt  2          processes   int sys argv 2       queues      a queue for each process     for pid in range processes           queues append  multiprocessing Queue         jobs        prev pipe   0     for pid in range processes           p   multiprocessing Process  target   getFileLineCount  args  queues  pid  processes  file name             p start           jobs append p       jobs 0  join    wait for counting to finish     lines   queues 0  get        logging info   finished    Lines     format  time time   - t  lines

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A lot of answers already  but unfortunately most of them are just tiny economies on a barely optimizable problem    I worked on several projects where line count was the core function of the software  and working as fast as possible with a huge number of files was of paramount importance  The main bottleneck with line count is I O access  as you need to read each line in order to detect the line return character  there is simply no way around  The second potential bottleneck is memory management  the more you load at once  the faster you can process  but this bottleneck is negligible compared to the first  Hence  there are 3 major ways to reduce the processing time of a line count function  apart from tiny optimizations such as disabling gc collection and other micro-managing tricks   Hardware solution  the major and most obvious way is non-programmatic  buy a very fast SSD flash hard drive  By far  this is how you can get the biggest speed boosts   Data preparation solution  if you generate or can modify how the files you process are generated  or if it s acceptable that you can pre-process them  first convert the line return to unix style   n  as this will save 1 character compared to Windows or MacOS styles  not a big save but it s an easy gain   and secondly and most importantly  you can potentially write lines of fixed length  If you need variable length  you can always pad smaller lines  This way  you can calculate instantly the number of lines from the total filesize  which is much faster to access  Often  the best solution to a problem is to pre-process it so that it better fits your end purpose   Parallelization   hardware solution  if you can buy multiple hard disks  and if possible SSD flash disks   then you can even go beyond the speed of one disk by leveraging parallelization  by storing your files in a balanced way  easiest is to balance by total size  among disks  and then read in parallel from all those disks  Then  you can expect to get a multiplier boost in proportion with the number of disks you have  If buying multiple disks is not an option for you  then parallelization likely won t help  except if your disk has multiple reading headers like some professional-grade disks  but even then the disk s internal cache memory and PCB circuitry will likely be a bottleneck and prevent you from fully using all heads in parallel  plus you have to devise a specific code for this hard drive you ll use because you need to know the exact cluster mapping so that you store your files on clusters under different heads  and so that you can read them with different heads after   Indeed  it s commonly known that sequential reading is almost always faster than random reading  and parallelization on a single disk will have a performance more similar to random reading than sequential reading  you can test your hard drive speed in both aspects using CrystalDiskMark for example     If none of those are an option  then you can only rely on micro-managing tricks to improve by a few percents the speed of your line counting function  but don t expect anything really significant  Rather  you can expect the time you ll spend tweaking will be disproportionated compared to the returns in speed improvement you ll see

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A one-line bash solution similar to this answer  using the modern subprocess check output function   def line count filename       return int subprocess check output   wc    -l   filename   split   0

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This is a meta-comment on some of the other answers   The line-reading and buffered  n-counting techniques won t return the same answer for every file  because some text files have no newline at the end of the last line  You can work around this by checking the last byte of the last nonempty buffer and adding 1 if it s not b  n    In Python 3  opening the file in text mode and in binary mode can yield different results  because text mode by default recognizes CR  LF  and CRLF as line endings  converting them all to   n    while in binary mode only LF and CRLF will be counted if you count b  n   This applies whether you read by lines or into a fixed-size buffer  The classic Mac OS used CR as a line ending  I don t know how common those files are these days   The buffer-reading approach uses a bounded amount of RAM independent of file size  while the line-reading approach could read the entire file into RAM at once in the worst case  especially if the file uses CR line endings   In the worst case it may use substantially more RAM than the file size  because of overhead from dynamic resizing of the line buffer and  if you opened in text mode  Unicode decoding and storage   You can improve the memory usage  and probably the speed  of the buffered approach by pre-allocating a bytearray and using readinto instead of read  One of the existing answers  with few votes  does this  but it s buggy  it double-counts some bytes    The top buffer-reading answer uses a large buffer  1 MiB   Using a smaller buffer can actually be faster because of OS readahead  If you read 32K or 64K at a time  the OS will probably start reading the next 32K 64K into the cache before you ask for it  and each trip to the kernel will return almost immediately  If you read 1 MiB at a time  the OS is unlikely to speculatively read a whole megabyte  It may preread a smaller amount but you will still spend a significant amount of time sitting in the kernel waiting for the disk to return the rest of the data

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An alternative for big files is using xreadlines     count   0 for line in open thefilepath  xreadlines      count    1   For Python 3 please see  What substitutes xreadlines   in Python 3

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You can use the os path module in the following way   import os import subprocess Number lines   int   subprocess Popen   wc -l  0   format  Filename    shell True  stdout subprocess PIPE  stdout  readlines   0  split   0        where Filename is the absolute path of the file

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How about this   import fileinput import sys  counter 0 for line in fileinput input  sys argv 1         counter  1  fileinput close   print counter

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Similarly   lines   0 with open path  as f      for line in f          lines    1

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Just to complete the above methods I tried a variant with the fileinput module   import fileinput as fi    def filecount fname           for line in fi input fname               pass         return fi lineno     And passed a 60mil lines file to all the above stated methods   mapcount   6 1331050396 simplecount   4 588793993 opcount   4 42918205261 filecount   43 2780818939 bufcount   0 170812129974   It s a little surprise to me that fileinput is that bad and scales far worse than all the other methods

[python] How to get line count of a large file cheaply in Python?

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